Static and Mobile Data Gathering Techniques in ...

Static and Mobile Data Gathering Techniques in Wireless Sensor Networks: A Survey Mukhtar M. Ghaleb, Shamala Subramaniam, Mohamed Othman, Zuriati Zukarnain

 

 

Static and Mobile Data Gathering Techniques in Wireless Sensor Networks: A Survey Mukhtar M. Ghaleb*, Shamala Subramaniam*, Mohamed Othman, Zuriati Zukarnain *Corresponding Author [email protected], [email protected] Department of Communication Technology and Network, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia 

Abstract Wireless Sensor Networks (WSNs) have continued tremendous by towards the change of human civilization from different perspectives which emerged as an effective solution for many applications. WSN consists of hundreds of tiny sensors which deployed in specific area for a specific purpose, while the data is the precious value stand behind the needs for WSN that play a significant role in human life. Communication between nodes in the deployment area is a must to send data from all sensors to reach the base station and then to the end user. One fundamental factor that affects energy dissipation for each sensor is determined based on the communication techniques used to collect and deliver the data which has a direct impact on the lifetime of the whole network. In this paper, we present a taxonomy of data gathering algorithms in WSNs for the static-based and mobile-based architectures. In addition, we present an overview of each technique, and highlighted the features and drawbacks of each one. An extensive survey was provided including the variety of existed data gathering technique.

Keywords: Wireless Sensor Networks, Data Gathering , Mobile Data Gathering, Multi-hop Data Gathering.

  1. Introduction  

Wireless Sensor Networks (WSNs) have positioned itself within the revolutionary network technologies as a cred- ible member. This is due to the enormous benefit on WSN in multitudes of fields that affect human life. Such as environmental applications like tracking birds movement and marine network for monitoring Corel reefs [1], Military surveillance [2]; Disasters monitoring like forest fire risk monitoring [3], fire system for Bord-and-Pillar coal mines [4], wildfire [5], volcano eruption [6]; Civil infrastructure [7, 8] such as building, bridges and areas; Health care [2, 9, 10]; and Monitoring Fish [2]. WSNs consists of a large number of sensor nodes which are tiny, lowpower, low-cost, short communication range, and limited processing and storage. In addition, each sensor node is powered by low-energy batteries [11]. These sensors typically used for sampling the surrounding environment, processing and may temporally storing the collected data and transfer the data to specific point such as the sink or the base station [12]. Most of the traditional WSNs architecture consists of stationary sensors communicate with each other in order to deliver the sensed data to the base station which is stationary too. In this architecture, the sensed data reaches the base station either by direct communication (single hop) or by experience multiple nodes (i.e. multi-hop) on the way to the base station. Due to the unique features of WSNs which brings many challenges [13] such as the lifetime of the network depends on the lifetime of sensor nodes and data aggregation is required to deliver data to the base station. Many researchers focused in another architecture which using Mobile Elements (MEs). In this architecture, the MEs gathers the sensed data from the sensor networks in order to enhance energy efficiency in WSNs either directly or via multi-hops. In this paper, we present a survey on data gathering techniques in WSNs with or without MEs. Based on our knowledge this article is the first one that combined the two architectures and study the advantages and disadvantages for both of them which is our motivation. The objective in this article is to study the data gathering techniques in WSNs regardless the carrier way for the sensed data which either static or mobile. In addition, the hybrid architecture which combined the static and mobile is included too. The contribution of this article is ensured by studying the three mention architectures and including new articles not included in the previous survey studies. To better understand the features of data gathering techniques (i.e., static or mobile), let us summarize these features in Table 1. The remainder of the paper is organized as follows. Section 2 introduces the components of wireless sensor networks and states the data gathering process. Data gathering schemes and taxonomy present and discussed in sections 3, 4, 5, 6 and 7. Finally, the paper concluded in section 8.

 

International Journal of Advancements in Computing Technology(IJACT)   6, Number 3, May 2014 Volume

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Static and Mobile Data Gathering Techniques in Wireless Sensor Networks: A Survey Mukhtar M. Ghaleb, Shamala Subramaniam, Mohamed Othman, Zuriati Zukarnain

 

Table 1. Data Gathering Features

  Nodes Base station Locomotion Connectivity Energy efficiency Reliability

Latency

Multi-hop Data Gathering Sensing the surrounding area and the main source of information It is stationary located inside the field or outside, and it is the destination of the information Most of the cases the nodes have no locomotion capability to overcome this problem a dense WSN is a must The sensor nodes expend their energy faster due to the multiple forwarding packets between nodes The probability of message loss and collisions is high The latency are negligible due to the high speed of relay packets between nodes

Mobile Element Data Gathering Sensing the surrounding area and the main source of information It is mobile and may remain stationary for a while before change location to new position Nodes may change their position The mobile element can cope with problem by visiting each isolated region The mobile element minimize the power consumption due to short communication range The mobile element may visit each sensor directly which eliminate the message loss and prevent the collision It depends on the velocity of mobile element which is very low

 

2. Preliminaries  

 

Basically WSN has three main components namely: the base station (Sink), regular sensor node, and special support nodes as depicted in Figure 1. These components are either static or mobile depends on the architecture of WSNs. • Sensor Node: Used to sense the surrounding environmental area such as temperature or pressure as they are the main sources of information. Each sensor equipped with four main parts [14]: Power supply, Sensor and analog to Digital Converter (ADC), Processor and Storage, and Transceiver. • Base Station (i.e., sink): The destination of the sensed information send by sensor nodes. The base station can receive the data directly from sensors through single hop or indirectly through multi-hop fashion. • Special Support Nodes: They are special nodes performing special tasks in order to enhance WSNs perfor- mance such as ME or working as intermediate data collectors between nodes and the base station. They are not either source nor the destination of data.

 

Data gathering must be defined before going further to elaborate the taxonomy . Thus, data gathering is the process to collect the data from all sensors all over the deployment area then deliver it to the base station regardless the way of how to collect the data or how to deliver it. Basically, these sensors sense the surrounded environment within their transmission ranges and communicate among each other to be able to send the sensed data to the nearest base station (if we have many). In general, we can summarize the data gathering process as follows. • The sensor nodes deploy in the deployment field based on the application requirements. • The sensors turn on their sensing capability to sense the surrounded area. • Processed and may store the sensed data for a while then send it to the base station based on the application used.

 

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Static and Mobile Data Gathering Techniques in Wireless Sensor Networks: A Survey Mukhtar M. Ghaleb, Shamala Subramaniam, Mohamed Othman, Zuriati Zukarnain

   

   

 

 

 

Figure 1. WSNs Components

• The raw data may send directly or through relay node to the base station based on the network paradigm used (i.e., Structure). • The raw data may send to a mobile node before reaching the base station (i.e., Mobile).

   

 

• The raw data may process at each level of hops in order to reduce the size efficiently (i.e., Data reduction).

3. Data Gathering Schemes and Taxonomy in WSNs In this section, we discuss the breakdown of the data gathering taxonomy as illustrated in Figure 2. In this break- down, four type of data gathering schemes are elaborated namely structure, mobile, data reduction, and deployment.

3.1. Structure Approaches As shown in Figure 2 the structure based data gathering can be achieved from two different approaches. The hierarchical based data gathering can exploit the sensor nodes to deliver the data to the base station and save the nodes energy. In this approach, the data need to traverse through other nodes before reaching the base station. Thus, some nodes selected as leaders take the burden to deliver the data to the base station. This scheme increases the network lifetime of avoiding the direct transmission to the base station. The hierarchical techniques can be further subdivided into four categories depending on the implementation of network architecture. The latter approach (i.e., flat) permissible to send the data packets directly to the base station without using the relay nodes.

 

3.2. Mobile Approaches The data gathering based mobile approach can be divided according to the element and the path used to deliver the data packets. Specifically, mobility pattern schemes address the mobile element pattern used to traverse through the deployment field aiming to save nodes energy. However, some of them can reduce the energy spent in communication as well, but it results in increasing the data gathering latency. In addition, mobility based address the type of element used to collect the data from the sensors such as the mobile sink or mobile relay as depicted in Figure 2.

 

3.3. Data Reduction Approaches Data reduction as shown in Figure 2 can be achieved by two different schemes. The process of innetwork aggre- gation consists of computing the MAX, MIN, AVERAGE, and SUM of some values as an example. These tasks are performed at intermediate nodes between the sources and the base station. Thus, based on the computing technique used the amount of data is reduced while traversing the nodes. There is no appropriate technique but it’s based on the application requirements. The latter scheme is the data compression applied at each node. This helped to reduce the amount of data packet sent by the source nodes. This scheme involves encoding information at the source node, and decoding it at the destination.

 

 

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Static and Mobile Data Gathering Techniques in Wireless Sensor Networks: A Survey Mukhtar M. Ghaleb, Shamala Subramaniam, Mohamed Othman, Zuriati Zukarnain

                                               

  Figure 2. Data Gathering Taxonomy  

 

3.4. Deployment Approaches The deployment scheme can affect the process of data gathering by two different approaches which address the issues of how to perform the deployment itself. These schemes are the area coverage deployment and the location coverage (i.e., target) deployment. The former ensures that each location within the deployment field must be covered by at least one sensor node. The latter defined a specific location for each sensor node to be attached to it.  

4. Data Gathering based on Structure  

In the previous section, we have discussed the data gathering as general approaches. In this section, we will survey the main proposal in the field of structure techniques for data gathering, for sake the simplicity the highlighted two parts are flat and hierarchical as depicted in Figure 2. In this schemes, the nodes have the ability to send their data packets to the base station through other nodes. Thus, some nodes will acts as local base station that received data packets from closer sensor nodes.

  4.1. Flat Structure In this case, the data needs to transmit directly to the base station as direct communication. It means that each sensor node send its data to the base station directly with no relay in between. Direct communication will require large amount of energy and this leads to expenditure the nodes energy faster and hence minimizing the network lifetime as observed by [15]. This is due to increasing the energy use for transmit a K-bit message from node to the base station depends on the distance d between them. This structure is not a preferred solution for many applications due to its limitations.

  4.2. Hierarchical Structure An emerging of data gathering based hierarchical is a promising solution leads to save more energy comparing to the flat structure. In this scheme, we can divided the hierarchical structure into four schemes namely, tree, cluster, chain, and grid.          

 

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Static and Mobile Data Gathering Techniques in Wireless Sensor Networks: A Survey Mukhtar M. Ghaleb, Shamala Subramaniam, Mohamed Othman, Zuriati Zukarnain

                                 

  Figure 3. Tree Structure Approach

Figure 4. Adjusting Tree Algorithm (ATA) [16]

 

 

4.2.1. Tree Structure

 

Data gathering based tree structure consider the commonly used one. In tree-based scheme, the base station (i.e., sink) has a data gathering tree rooted at itself. it can be a localized minimum spanning tree including all the sensor nodes as depicted in Figure 3. Shen in [16] proposed a Tree-based Energy and Delay Aware Scheme (TEDAS). In this proposed scheme, the Minimum -Expected Transmission Count (ETX) - Spanning Tree (MEST) is constructed first, then the delay bound is satisfied by adjusting the tree to improve network lifetime by pruning and grafting a sub-tree to a target node via Adjusting Tree Algorithm (ATA). The ATA attempt to find the bottleneck (i.e., lowest energy) nodes and alter their position in the MEST so that the lifetime of the WSN is improved. Obviously, the shortest path is not guaranteed in this scenario, this scenario illustrated in Figure 4. In [17], the authors proposed two algorithms for data gathering based on tree structure. The first is based on Minimum Spanning Tree (MST) and the other one based on Single Source Shortest Path Spanning Tree (SPT) algorithm. These two algorithms used to solve the problem of minimum energy data aggregation enhanced convergecast (DAC). The authors in [18] attempts to achieved reliability and maximize network lifetime by examine the load balancing between nodes during the data gathering stage. In this proposed scheme, a set of parent nodes defined for each single node that determine the minimum hops path to the base station. Thus, data packet send in dynamic routing where each node select the parent based on parent selection function σ to forward the sensed data. The σ is defined based on the current state of the network and the two routing algorithms, Max-min Path Energy (MPE) and Weighted Path Energy (WPE), that aim to ensure load balancing among nodes.

4.2.2. Cluster Structure In the cluster structure, the nodes grouped as clusters, each cluster has a Cluster Head (CH) working as local base station. Each CH has the ability to received the data from all sensors in the cluster area using short transmit distance. In addition, the CH take the burden to transmit the data to the base station even directly or through other CHs such as LEACH [15]. Low-Energy Adaptive Clustering Hierarchy (LEACH) is a first clustering based protocol. LEACH used randomized rotation of CH to distribute the consuming energy fairly in all sensor nodes. Based on residual energy a sensor node will elect to be a CH acting as local base station that aggregate the local data and send it to the base station as depicted in Figure 5. Since there are a few nodes will communicate with the base station will be affected based on the transmitting distance. Due to rotation process in the CH (i.e., CH not fixed) load energy will distributed fairly in whole scenario. However, LEACH assume that all nodes has the ability to send the data to the base station wherever they are. In addition, considerable energy consumption on the cluster formation is unsolved problem [19]. Overall, LEACH achieved 8 improvement in comparison with direct transmissions [20].        

 

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Static and Mobile Data Gathering Techniques in Wireless Sensor Networks: A Survey Mukhtar M. Ghaleb, Shamala Subramaniam, Mohamed Othman, Zuriati Zukarnain

                                   

Figure 5. Cluster Head Approach 

      Figure 6. Hierarchical Cluster Head Approach  

 

In order to enhance energy consumption a number of approach are proposed such as LEACH-C [21], Adaptive LEACH [22]. As clustering techniques itself utilized to extend lifetime network by applying data aggregation and balancing energy consumption between sensor nodes [11]. In [11] the authors applied harmony search algorithm as basis to choose cluster heads that minimizing the intra-cluster distance and hence optimizing the energy of the network. However, all these algorithms [15, 20, 21, 22] are of single-hop in nature, where all cluster-heads directly communicate with the base station. Based on [23], The main disadvantage of these single-hop clustering techniques is that the energy dissipation can be very high for long distance transmission between cluster-heads and the base station. For those who wants to know more about clustering algorithm and how to choose cluster heads please refer to [24, 25, 26, 27, 28]. Another improvement based on clustering is ability to work with multi-hop data gathering as depicted in Figure 6. In this scheme all sensors grouped as cluster with cluster head. And all cluster heads able to connected in order to serve each other by carrying data to the base station such as Hybrid EnergyEfficient Distributed clustering (HEED) [29], Power-Efficient and Adaptive Clustering Hierarchy (PEACH)[19], and Multi-hop Relay LEACH (MR-LEACH) [30]. While clustering provides an effective way for data gathering in wireless sensor networks [31] and Cluster Heads can aggregate the data collected by sensor on its cluster. Thus, this will decrease the number of relayed packets [32] and hence maximize network lifetime by decrease energy consumption in order to send packets to the base station. Many improvements occurred on clustering such as energy-balanced unequal clustering [33] which partition all sensor nodes into clusters with unequal size, the clusters beside the base station should be smaller than the cluster far from the base station. The cluster heads of smaller clusters should preserve some energy to serve other cluster heads which they are far from the base station (i.e., inter-cluster relay traffic) to avoid hotspot area which closer to the base station. Another enhancement of data gathering by clustering is use energy-aware multilevel clustering by abstracting the nodes as root tree which has performance of minimal relay set and the maximal weight according to graph theory [23] this divided the cluster heads to multi levels for examples CHs for level 3 send its gathered data to CH level 2 and CH level 2 send its gathered data to CH level 1 and subsequent to the base station. Another approach leads to more uniform energy consumption in the nodes and prolong their lifetime is by initialized nodes with different energy level [31], this approach guarantee shorter waiting time for CHs competition by minimize communication between nodes to elect CH as those nodes rich in energy. This lead to balancing energy and maximize network lifetime. Sasaki in [34] proposed a new data gathering scheme based on Set Cover Algorithm on mobile sink. In this scheme, the authors used new clustering methods based on communication range of each node to gather data from all nodes. Then choose least number of cluster which they cover all sensors selected by set cover algorithm. Furthermore the proposed scheme solve the travel salesman problem to determine the path of mobile sink between cluster heads. The objective of this scheme was jointly between fairness of gathered data and higher efficiency of data gathering.

 

 

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Static and Mobile Data Gathering Techniques in Wireless Sensor Networks: A Survey Mukhtar M. Ghaleb, Shamala Subramaniam, Mohamed Othman, Zuriati Zukarnain

                                             

Figure 7. Chain based Approach (PEGASIS) [20]

 

 

 

 

 

 

             Figure 9. Grid Approach [37] 

Figure 8. Grid-Chain based data gathering [36]

4.2.3. Chain Structure Power-Efficient GAthering in Sensor Information Systems (PEGASIS) [20] is a near optimal chainbased protocol that is an improvement over LEACH. In PEGASIS, each node communicates only with a closer neighbour. Gathered data moves from node to node, get fused, and eventually one designated node takes the responsibility to transmitting the combined data to the base station in each round as depicted in Figure 7. Nodes take turns transmitting to the base station. So that reducing the average amount of energy spent per round. However, based on [35] the probability of long chain is high, data transmission will create time-delay, and the method to choose the cluster head is not suitable for load balancing. Huang in [36] proposed energy efficient grid-chain based data gathering scheme. In this scheme, the deployment area divided into different area as depicted in Figure 8. In addition, each nodes within a single area communicate with each other and choose the Grid Node (GN) randomly by turns to take the burden of sending the data to the next cell as a part of establishing a chain.

  4.2.4. Grid Structure The deployment area is partitioned into several two dimensions logical grids, each grid is a square of dd. Hwang in [37] proposed a grid-based data gathering which adopt hierarchical grid structure and constructs cycles by connecting heads in each order of the hierarchy as depicted in Figure 9.  

5. Data Gathering based on Mobile Element  

In this section, we will discuss the data gathering based mobile element approaches as defined in the previous section. There are two broad schemes of mobile data gathering, mobility based and mobile element used. Each one of them have more divisions as depicted in Figure 2.

  5.1. Mobility Pattern The mobile data gathering latency relay on the velocity of mobile collector and the tour path of collecting data from deployed sensors. The mobility patterns can be classified into three types, planned path, fixed path and random path. The next subsections fairly described those types.        

 

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Static and Mobile Data Gathering Techniques in Wireless Sensor Networks: A Survey Mukhtar M. Ghaleb, Shamala Subramaniam, Mohamed Othman, Zuriati Zukarnain

5.1.1. Planned Path Limin Sun in [38] designed a moving strategy for mobile sinks in order to eliminate the hotspot area around static sink. In this approach, the authors proposed a half-quadrant-based moving strategy for mobile sinks in data gathering sensor networks. In the half-quadrant-based algorithm the sink chose its current position as origin and divided the coordinate system in to half-quadrants. The sinks move from one quadrant to another based on higher residual energy of sensors and choose the path carefully to avoid quadrants with lower energy sensor. Yanzhong Bi in [39] designed two movement schemes for mobile sink in data gathering sensor network. In the first scheme, the mobile sink move to new sojourn position based on the residual energy of sensor nodes in one step to consume their energy by forcing it to forward data as much as possible. In the other scheme, the mobile sink approaches the sensor of highest residual energy step by step depends on the velocity of the mobile elements. Meanwhile avoiding passing by middle nodes with lowest residual on the moving path. Both schemes results in extend network lifetime by distributed consumed energy more evenly between sensors. Ma and Yang in [40] design a movement path planning algorithm of mobile collector (SenCar) by finding a turning points, which is adaptive to the sensor distribution in the field. This SenCar can avoid any obstacles on planning path. When the SenCar moves through this planning path the sensors organized into spanning tree in order to send their packets to SenCar through multi-hop fashion, which depends on the location of the sensor and line of the movement path. This designed algorithm deals with three important factors, which are the load balancing, movement planning, and clustering. Saad in [41] proposed a movement strategy for mobile sink in hierarchical, large scale network. The mobile sink traverses the clusters heads among field based on well panned moving path strategy. In addition, the mobile trajectory planned with no require a multi-hop relay between cluster heads to reach mobile sink in order to minimize energy consumption. Ruiyun Yu in [42] proposed two Grid-Based Mobile Elements Scheduling (GBMES) that schedule the mobile el- ements periodically to gather the data from sensors in partially connected sensor networks. The authors here assumed that the network is not fully connected and its divided to sub regions (portioned in to square grid cell), each sub region or grid cell a tree called (Multi-PintRelay MPR tree) rooted at the sensor node nearest to the geometric centre of the grid cell is constructed. The main purpose of GBMES is to avoid data loss due to buffer overflow of sensor nodes by scheduling the movement of mobile elements.

  5.1.2. Fixed Path The authors in [43] applied multiple data mules (i.e., mobile element) with load balancing among sensors are moving in straight lines. These mules nominate one of them as a group leader and will be responsible for classifying the nodes being either shareable or non shareable nodes. Subsequently, each mule is assigned to a number of sensors to serve them. Mules have the ability to ensure load balancing between all sensors.

  5.1.3. Random Path In this subsection, the mobility for the mobile element is random which based on factors depends on the technique used, for example [44] applied the two-dimensional random walk model for the mobility. Shah in [44] applied Data MULEs (Mobile Ubiquitous LAN Extensions) using three-tier architecture modeling. In this model, the authors achieved cost-effective connectivity in sparse networks while reducing the power requirements at sensors. The key issue is by using mobile agent MULEs that capable of short range wireless communication and exchange the data from nearby sensor. The MULEs can pick up data from the sensors when it is in close range, buffer it and drop it in the final terminal at base station. The primary advantage of this approach is a potential saving energy that occurs on all sensors because of short range communication. However, this approach lakes from high latency as the sensors should wait for MULEs to carry their data. And because of continuous listening of all sensors looking for MULEs when pass by a potential energy consumed during radio listening. This approach leads to unexpected failure such as loss of a MULE or inability to reach sensors because of change in path causing limitation in mobility. Depends on application requirements this approach appropriate for high latency data delivery. Pazzi in [45] proposed a mobile data gathering for delay-sensitive applications such as emergency preparedness and hostile environment surveillance. In this approach, the mobile data collectors traverses the monitored area and send a beacon periodically. Sensor nodes that received beacon will send a join request to MDCs cluster and update their routing information in order to relay packets to MDCs. The main objective here is to reduce delivery packet delay and increase

 

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Static and Mobile Data Gathering Techniques in Wireless Sensor Networks: A Survey Mukhtar M. Ghaleb, Shamala Subramaniam, Mohamed Othman, Zuriati Zukarnain

reliability by reducing number of hops the data packets have to traverse. In order to enhance reliability the authors employ both mobile and static sink. A sensor node will have a route to a static sink and a dynamic. That means temporary route to the nearest mobile sink to transmit the data any time if its available. Otherwise, the route to static sink will be used instead.  

5.2. Mobility Based The mobile collectors traverses the deployment area and collect the sensed data from the respective nodes during pause at some location or movement. In general, the mobile collectors divided into two types, mobile relay (i.e., nodes or mobile element) and mobile sink. The next two subsection describe those types in more details.   5.2.1. Mobile Relay Ming Ma and Yang in [46] proposed a new data gathering algorithm by applying mobile data collectors. M- collectors can gather the data from sensors without any relay that means through single hop only. The authors work with one mobile collector and also with more than one mobile collector to enhance scalability and solve intrinsic problem of large scale homogeneous networks. The main idea here is that how to find a minimum polling points for mobile collector with capable of reach every sensor in the network with only single hop by using covering salesman problem. In addition, they applied a TSP for a tour path of mobile collector. However, this approach leads to energy efficiency by minimizing energy used to transmit data between sensors and mobile collector because of short trans- mission distance used. But, this will lead to higher latency specially with dense and sparse network as the authors ignore the uploading time. Moreover the authors here did not solve the issue of how to schedule between multi-mobile collectors to deliver the data to the sink in efficient way. Azzedine in [47] attempt to eliminate bottleneck and high traffic load around the static sink by applying light weight mobile data collector. In this approach, the mobile entity periodically broadcast beacons. Once the sensor received it then decided whether to join or not, the cluster based on hop level as the mobile collector play role of cluster head. Sensor nodes use signal strength of the beacons in order to perform a simple but efficient route re- configuration. By reducing a number of hops a data packet must traverse, the authors insure reducing delay and increasing reliability with little overhead. However, this approach use a hybrid strategy to collect data with mobile data collector and static sink. Nodes should not wait until mobile collector come nearby but each node maintain hop level to both mobile collector and static sink. In[48] the authors proposed heterogeneous network consists of a large number of sensor nodes and a few Data Collectors (DCs). DCs have locomotion capabilities (i.e., mobile) with controlled mobility, DCs are distributed over the sensing area. The DCs collect the data from nearby sensors through a multi-hop fashion and communicates between each other to send the data to the base station. Each DC changes its location in such a way a forwarding load is balanced among sensor nodes.   5.2.2. Mobile Sink Marta and Cardei in [49, 50] proposed a sink mobility that solve the energy holes near the sink. Due to relaying data of other nodes, the energy level of sensor nodes nearest to sink will deploy their energy faster. The authors proposed a sink movement to new location based on energy level of sensors nodes in that location. When the energy level of nearest nodes becomes low the sink should change the position to another location again. The authors here improved network life time by using not only a predetermined path of a sink movement along the perimeter of hexagonal tailing but also with unrestricted mobility path based on applications requirements. The authors in [51] proposed a rendezvous-based data collection. In this approach, the mobile base station visits subset of nodes (i.e., rendezvous points) and collect the data through a singlehop fashion with a restricted tour path(i.e., no longer than L meter). However, despite this approach to minimizing the latency by restricting the mobile base station tour path, it is suffering from power consumption due to unbounded local data caching.    

 

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Static and Mobile Data Gathering Techniques in Wireless Sensor Networks: A Survey Mukhtar M. Ghaleb, Shamala Subramaniam, Mohamed Othman, Zuriati Zukarnain

6. Data Gathering based Data Reductions In this section, we will continue the survey by introducing some of the data reductions techniques used in the data gathering. This reduction represented by in-network aggregation and data compression as depicted in Figure 2.9  

6.1. In-network Aggregation Xing in [52] propose a rendezvous based approach in which a subset of nodes serve the other nodes as rendezvous point (RP). These RP buffer the data from other nodes and wait for mobile element to upload the data. This approach attempt to balance between energy consumption at sensor nodes and latency to deliver the data to the base station. The authors here designed two algorithms with mobile elements paths constraint to the routing tree and the other one with no constraints on mobile elements path. In order to handling unexpected delays in mobile element because of mechanical problem or obstacles on its motion path, the authors designed two mechanisms. The first one called Safe Waiting and the second one called online path adaption. Based on time constraints, RP may send data through mobile elements or simply through multi-hop to reach the base station. However, the authors constraint the delay but did not constraint the maximum hop for traverse data to reach RP. Also the authors assumed that the buffer of each RP can handle all the traffic coming with no losing data.  

6.2. Data Compression The data compression is to reduce the amount of bits transmitted [53] due to the limited energy power. The authors in [53] proposed a dynamic algorithm based on adaptive dictionary. In this algorithm, the temporal data correlation and Huffman compression is exploit. The authors in [54] Exploiting the natural correlation that exists in data collected by sensors and the principles of entropy compression. The main task of this algorithm is particularly suited to the reduced storage and computational resources of a WSN node.

 

7. Data Gathering based Deployment In this Section, we complete the survey by introducing the last data gathering scheme based deployment. An inherent concern for a wireless sensor network (WSN) is the coverage problem [55]. Coverage problem basically is caused by the reasons, not enough sensors to cover the whole area of interest, limited sensing ranges and random deployment [56]. There are two types of deployment which based on application requirements as discussed earlier, area coverage and location coverage (random) as depicted in Figure 2. These deployment based on the application requirements and the type of objects to be monitored.  

7.1. Area Coverage The authors in [57] proposed an algorithm to maximize the total network coverage while in the same time ensure the connectivity among all nodes. In this algorithm, the nodes deployed one at a time, each node making benefits from the information gathered by the previously deployed nodes to determine its ideal deployment location. The authors in [58] proposed an area coverage protocol which aims to turning of some sensor nodes while ensuring the full coverage of the area. In addition, providing K-area coverage means that every physical point of the monitored field is sensed by at least k sensor nodes. The authors in [55] proposed a mobile robot to assist the initial sensor deployment to enhance and solve the coverage problem by eliminate the coverage holes. In addition, this approach attempt to relocate the sensors from densely deployed area to the sparsely deployed areas where the coverage holes exists.  

7.2. Location Coverage (Target) The authors in [59] proposed a method in which that cover a set of discrete targets in deployment field. In this method, k-angle coverage is considered by guarantee that any target in the deployment field should be covered at least by K-sensors. The authors in [60] address the problem of multiple target coverage problem (MTCP). This is done by proposed an energy efficient sensor scheduling scheme for MTC by considering the number of targets covered by the sensor and the redundancy of overlapped targets. Thus, different sensors will monitor different numbers of targets.

   

 

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Static and Mobile Data Gathering Techniques in Wireless Sensor Networks: A Survey Mukhtar M. Ghaleb, Shamala Subramaniam, Mohamed Othman, Zuriati Zukarnain

8. Conclusions In this paper, we present different techniques that affects data gathering process in wireless sensor networks. Furthermore, the design of this survey is flexible so it can be easily understand to the researchers. It is hoped that this survey will be able to serve all researchers when studying the data gathering techniques with clarity, precision, and efficiency The main objective behind these techniques is to reduce the amount of energy consumption at each sensor node and hence maximize network lifetime. It is obvious that we could found one or more technique that implemented together are merged in one article, for instance, the authors could study the mobile data gathering with in-network aggregation and applied a planned path.  

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