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ScienceDirect Procedia Computer Science 77 (2015) 176 – 183

ICTE in Regional Development

Dynamic Coordinator Mobility Management Methodology for Balancing Energy Consumption in the Wireless Sensor Network Aleksejs Jurenoksa*, Michael Boronowskyb a

Riga Technical University, Kalku str.1, Riga, LV-1019, Latvia b University of Bremen / TZI, Gemrany

Abstract Nowadays, the most topical researches pertaining to wireless sensor networks are based on the optimization of the structure of network transmission protocol, the routing and network optimization and as a result it is possible to prolong the lifecycle of wireless sensor networks. The nodes pertaining to information storage and processing are mainly equipped with an uninterrupted power supply, independent distribution network connectivity and a high performance computing system. This means that the direction of the data is definitely known in the sensor network, the information from terminals is sent to the information storage and processing nodes. The capacity of data traffic near the coordinator node is much higher than at the distant points, as a result, the existing elements close to processing nodes stop operating sooner than others due to lack of electricity and therefore the network ceases to function. This article describes the management methodology of coordinator node mobility of the in a wireless sensor network which provides the wireless sensor network node with grouping, the transmission protocol optimization and the adjustment to the current environment, resulting in a reduction of the power consumption in the network nodes. © by Elsevier B.V. This is an open access article under the CC BY-NC-ND license © 2015 2016Published The Authors. Published by Elsevier B.V. (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the Sociotechnical Systems Engineering Institute of Vidzeme University of Applied Sciences. Peer-review under responsibility of the Sociotechnical Systems Engineering Institute of Vidzeme University of Applied Sciences Keywords: Sensor Network; Life circle; Data processing; Network dynamic node

* Corresponding author. E-mail address: [email protected]

1877-0509 © 2015 Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the Sociotechnical Systems Engineering Institute of Vidzeme University of Applied Sciences doi:10.1016/j.procs.2015.12.380

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Aleksejs Jurenoks and Michael Boronowsky / Procedia Computer Science 77 (2015) 176 – 183

1. Introduction The main drawback of a wireless sensor network is the low power batteries which significantly limit the life expectancy of the network. There is unequal power consumption in the network nodes which results in the network becoming unable to function when there is a lack of electricity in some network nodes despite the fact that most of the network still continues to operate. The structure of a wireless sensor network depends on sensor network functions. By defining sensor network nodes it is possible to distinguish between three states, which a network element may be located in: x Terminal device: a node that collects data from the sensor modules, processes the information and transmits the necessary result within the network2, 13. x Router: a network element that provides the retransmission of the received information from the terminal devices to the main data collection point6, 11. x Coordinator: an information collection unit which ensures the collection and transmission of information from all the network elements to the application of the highest level through the physical network7, 8. A sensor network can be defined as the mathematically oriented amount of graph G where each graph G consists of vertices ൌ ሼͳǡʹǡ ǥ Ǥ ݉ሽ and edgesܷǣ ܷܸ ‫ ܸ כ‬and the total energy consumed in all graph nodes P4. ‫ ܩ‬ൌ ሼܸǡ ܷǡ ܲሽǤ

(1)

In reality, the coordinator node in wireless sensor networks can be located in any position of the network graph3, . There are situations when it is possible to predict the next location of the coordinator node, but sometimes the position of the coordinator node is selected at random. As a result, the possible position of the network graph can be expressed by the formula: 4, 6

݉ ௠Ǩ ȁܸ௦ ȁ ൌ ቀ ቁ ൌ , ௦Ǩሺ௠ି௦ሻǨ ‫ݏ‬

(2)

where s is the amount of coordinators in the network and ‫ ݏ‬൑ ݉ǡm is the amount of vertices in the network. When assessing the life expectancy of a wireless sensor network, the initial power of the network coordinator is considered to be unlimited and it can be expressed using the formula: ܲ௜ ሺሻ ൌ ሺ‫ܧ‬௜ ሺሻ ՜ λሻሾͶሿ. This article describes the management methodology of the mobility of a coordinator node in a wireless sensor network which provides the wireless sensor network node with grouping, the transmission protocol optimization and adjustment to the current environment, resulting in a reduction of power consumption in the network nodes. 2. Related works Using information provided in other scientific works2, 7, it can be argued that recently in the field of wireless sensor network research there have been three topical research directions: x The organization of the information flow in a network that is related to the increase in life expectancy of a wireless sensor network5. It is necessary to point out that the transmission flow depends on the network usage and the network operation scenario. x The adaptation of power sources to use alternative energy in wireless sensor networks5, 9, 11. x Studies related to the increase of life expectancy of a wireless sensor network using non-modified wireless sensor networks6 According to the works of some authors3, 4, 5 the use of metrics in the traditional network router protocols is offered and it increases network transmission capacity and prevents data transmission delay. Metrics functions can be performed by a number of intermediate network nodes to the final objective, the capacity of the communication

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channel and the line load level13. The remaining node power metrics are often used in sensor networks to track the data processing unit. In this case, from different alternative routes the one with the largest amount of energy remaining will be selected. Nowadays, the use of the mobility of network components is considered to be a prospective balancing method. In various works10, 12 different authors showed that the use of mobility brings more benefits to the increase of autonomous operations of a sensor network. Therefore, this approach will be thoroughly discussed in the following paper. 3. Planning the movement of the dynamic coordinator node One of the factors that affect the changes in network configuration is the relocation of the coordinator in the network3. By reducing the need to change the configuration the remaining amount of energy in the network nodes will increase and, as a result, the total network life expectancy will be increased4,5. In the case of a network composed of one coordinator node it can be assumed that the network operates via a directed graph structure3. The information from all the network elements is delivered to an object using some network nodes in order to transmit information. As a result, a factor that affects the reduction of the network life expectancy is the network topology change. The reconfiguration of every network consumes the energy in the node for creating new routing paths. When planning the movement of the coordinator, the first task is to reduce the frequency of reconfiguration and to create a data flow path to strain all of the all network elements as evenly as possible. 3.1. The mobility of the network coordinator There are two types of coordinator mobility in a functioning system: x Expected location of the coordinator: the coordinator has a cyclical operating pattern and the path of the coordinator in the network is determined as well as it is known how long the coordinator will be located at each position; x Random location of the coordinator: the place for the coordinator is defined by the user or in other cases the system generates a random location for the coordinator within each network start-up cycle. The time which the coordinator will spend at the same location is dependent on the requirements described in the algorithm of the performance. As a result, the time ‫ݐ‬௡௘௧ that is necessary for the coordinator to go through the entire trajectory is: ௠ ሽǡ ଵ ଶ ‫ݐ‬௡௘௧ ൌ ሼ‫ݐ‬௡௘௧ ǡ ‫ݐ‬௡௘௧ ǡ ǥ ǡ ‫ݐ‬௡௘௧

(3)

௠ where: ‫ݐ‬௡௘௧ the time the coordinator spends at a certain position. Each location of the coordinator is associated with a new network configuration. The system identifies the absence of the coordinator in the existent path structure and performs reconfiguration of the network providing a new route from the nodes to the coordinator. Let us look at the possibility of mobility management through network reconfiguration. Suppose that the original location of the coordinator is known and the motion path of the coordinator is strictly defined. Whilst repositioning ௠ of the coordinator changes. It is known3, 4, 10 that via the transmission of within a network, the spatial location ܸሺ௫ǡ௬ሻ data the object consumes power which depends on the distance, but is not smaller than the technically defined transmission powerܲ௧௫ as well as the reception powerܲ௥௫ Ǥ When planning the network structure and the location of the network nodes as well as knowing the movement pattern of the coordinator it is possible to divide the network structure into segments ܵ௡௘௧ . Consequently, it can be ௠ ଵ ଶ ǡ ܸሺ௫ǡ௬ሻ ǡ ǥ ǡ ܸሺ௫ǡ௬ሻ ൟǤ assumed that ܵ௡௘௧ ‫ א‬൛ܸሺ௫ǡ௬ሻ

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When the coordinator is located in one of the segment’s ܵ௡௘௧ vertices, the transmitter power of the elements located on it will not change since the effective range is not changed. The total power consumed in each operating cycle ‫ݐ‬௖ depends on the sector ܵ௡௘௧ where the coordinator node is located. It is not useful to reconfigure a network at the beginning of each network operating cycle if the network segment remains unchanged. If the node control module [3] does not reconfigure a network due to the remaining energy ‫ܧ‬௡ , then the reconfiguration of the network will take place if the segment ܵ௡௘௧ , which the coordinator is ௡ ௡ିଵ ് ܵ௡௘௧ Ǥ located in, is changed ܵ௡௘௧ In the second operational scheme the coordinator can use a randomly selected network location which is ௔ ௡ ଵ ‫ ב‬൛ܸሺ௫ǡ௬ሻ Ǥ Ǥ ܸሺ௫ǡ௬ሻ ൟǤ determined by the user - ܸሺ௫ǡ௬ሻ The results show an additional condition, which facilitates network reconfiguration: The network topology is changed in case the condition is fulfilled (the position of the coordinator is changed) ௔ ௔ିଵ ് ܸሺ௫ǡ௬ሻ . ܸሺ௫ǡ௬ሻ 3.2. The remaining amount of energy in the nodes The second group of factors that impact the change in the network structure is the remaining amount of energy in each of the network elements. The completion of each operational cycle ensures the reduction of the remaining amount of energy of the network elements. It can be assumed that at the beginning of each cycle the remaining energy can be regarded as the initial energy ‫ܧ‬଴௡ ൌ ‫ܧ‬଴௡ െ ‫ܧ‬௔௡ , where ‫ܧ‬௔௡ െenergy which is consumed in the node n within the active interval ‫ݐ‬௖ . The remaining amount of energy in the nodes differs for a functioning network4. The closer the node is located to the coordinator point, the smaller the remaining amount of energy in the node. It is known3 that in order to reconfigure a network additional amount of energy in each node as well as time is required. As a result, it is not useful to reconfigure the network before each operating cycle. A new variable ‫݌‬௠௜௡ should be introduced and it will indicate the limit when the network reconfiguration is necessary. ‫݌‬௠௜௡ ൐ ݉ܽ‫ݔ‬௜ ܲ௜ െ ݉݅݊௝ ܲ௝ , where i,j = 1,2,…,n.

(4)

The total amount of energy consumed by the network is calculated by forming a network topology and using a determined action scheme of a network; the amount does not change during the entire time of a functioning topology. 4. The management method of the dynamic coordinator A network node control module has been introduced in a functioning system where the module controls the conditions that contribute to the reconfiguration of the sensor network in the nodes: ௔ ௡ ଵ ‫ ב‬൛ܸሺ௫ǡ௬ሻ Ǥ Ǥ ܸሺ௫ǡ௬ሻ ൟǤ (5) 1. The location of the coordinator changes: ܸሺ௫ǡ௬ሻ 2. A large difference in the remaining energy of the network elements is found:‫݌‬௠௜௡ ൐ ௜ ܲ௜ െ ௝ ܲ௝ where i,j = 1,2,…,n.

The node management module has two functions in an operating system: x To ensure the collection and storage of the remaining amount of energy in the nodes at a certain step ‫ݐ‬௖௡ . x To determine the period ‫ݐ‬௖௡ where it is necessary to carry out the reorganization of the routing path. Unlike the network sensor elements the control module of the mathematical node is multifunctional. Some tasks of the module can be executed simultaneously. Each executable cycle or condition is implemented as an independent operation. An operation’s scheduler is cyclically functioning in the control module of the node and it

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executes the tasks on the network regardless of the situation. This approach allows to coordinate the functions of the control module as well as to manage every executable process. In the proposed model each network coordinator node has a local node management module which provides reciprocity of the node in the network. 4.1. The localization mechanism of the network node management module The network node control module promotes the state and changes of a network and the module operates on a protocol level. The localization mechanism of the module is divided into 3 phases: distribution phase, communication phase, operational phase. 4.1.1. The distribution phase of the node control module The node control module provides communication dependence coefficient between nodes i and all other network nodes whilst performing certain (predefined) monitoring activities. Communication dependency ratio ܴ௜ே between the nodes i and N is determined using the formula: ܴ௜ே ൌ

஼೔ಿ ஼೔೘

ǡ ‫݉ ് ܰ݁ݎ݄݁ݓ‬Ǥ

(6)

When the maximum ܴ௜ே value exceeds the defined limit factorߠ the allocation phase of the node control module includes the visible node into the orderly system node group: ݇ ൌ ܽ‫݃ݎ‬௝ ݉ܽ‫ݔ‬ሺܴ௜ே ሻ ሺܴ௜௞ ൐ ߠሻoܽ௜ ‫ܩ א‬௞ ǡ

(7)

where: ܽ௜ is the copy of the node control module i, ‫ܩ‬௞ – the group of the node control module k, ܽ‫݃ݎ‬௝ – a function that returns the value j which equals the maximum value of the factor ܴ௜ே . 4.1.2. The communication phase of the node control module Whilst the node operating module is functioning, the communication in a network happens via the technical information of the OSI level protocol. By using the formula no.10 and the gathered information ‫ܥ‬௜ே ሺ‫ݐ‬ሻ the node’s ability to communicate and transmit the necessary network information is analyzed. The whole communication model of the node control module can be defined as: ‫ ݅ܯ‬ൌ ሼܵܽǡ ܵ݁ǡ ܵ‫݌‬ǡ ‫ܮ‬ǡ ܵ‫ܽݎ‬ሽǡ

(8)

where: Sa – the amount of nodes ܵܽ ൌ ሼܽଵ ǡ ܽଶ ǡ Ǥ Ǥ ܽ௡ ሽ that are a part of the communication process, Se – the amount of communication environment sectors where everything functions, Sp – the amount of congested sectors in the network where the nodes consume the largest amount of energy, L – the length of the path from the coordinator point to the network node, Sra – the amount of the routing nodes for the node control module to use for transmitting information to the management node. 4.1.3. The operating phase of the node control module The management module of the node performance operates cyclically regardless of the position of the node. When the node is set to standby or sleep mode the network node control module switches to the power-saving mode ௔௚௘௡௧ and monitors the network using the defined time ‫ݐ‬௠௜௡ of the system. When the node is in active mode the main task of the control module is to initialize the network topology changes taking into account 3 conditions: x the total network energy consumption exceeds the limit ‫݌‬௠௜௡ ൐ ௜ ܲ௜ െ ௝ ܲ௝ where i,j = 1,2,…,n.; x the value of the node ܴ௜ே exceeds the defined factorߠ limit; x It is not possible to establish a communication link with other objects in the network.

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Within the initialization phase of the node management module the total communication model ‫ ݅ܯ‬of the network nodes is defined in accordance with the formula no.8. Depending on the operational state of the node, whilst the node module is operating cyclically, the intensity of information between the nodes ‫ ݊݅ܯ‬is set. In case if the visible node of the communication factor ܴ௜ே ൐ ߠ is set on the list of nodes with the lowest metric for creating the paths, the cycle repeats as long as there is the possibility to define the communication path. 5. The use of the dynamic coordinator within a network When working with actual systems there are some restrictions that do not allow to prepare all the necessary information in order to model the life expectancy of a system: x It is impossible to find out the precise actions of the network coordinator, whether it will stagnate and whether it will use the expected movement trajectory or location. x The power ‫݌‬௞௜ consumed in the nodes can change over time. In an operating system there might be a situation where it is impossible to establish a communication route between the sensor node and the coordinator3. As a result, the energy consumption is increased which provides inefficient means of communication between the terminal nodes and the coordinator. To increase the life expectancy of the network the virtual coordinator node should be introduced; it moves within the network cyclically using a familiar motion trajectory. Additional labels should be introduced for the description of the algorithm of a virtual coordinator: ܵሺ݇ሻ – A set of graph ‫ܩ‬௦ vertices which includes the adjacent vertices of the k and k vertices: ܵሺ݇ሻ ൌ ሼ݇ሽ ‫ ׫‬ሼ݆ǣ ሺ݇ǡ ݆ሻ ‫ܧ א‬௦ ሽ.

(9)

ܵሺ݇ሻ – The amount of nodes that are physically connected to the virtual coordinator node k and is located in its vicinity: ‫ܦ‬ሺ݇ሻ ൌ ݅ ‫ܸ א‬௡ ǣ ሺ‫ݑ‬ǡ ݅ሻ ‫ܧ א‬௡ ሺ݇ሻǡ ‫ܸ א ݑ݁ݎ݄݁ݓ‬௡ െ ܿ‫݁݀݋݊ݎ݋ݐܽ݊݅݀ݎ݋݋‬.

(10)

In every step n when the coordinator is located in one of the positions ‫݌‬௡ it chooses a new position from the list ܵሺ‫݌‬௡ ሻ in order to take the next step ‫݌‬௡ାଵ based on the remaining amount of energy in the nodes. If the network management module facilitates the change of network topology based on the conditions described in the article, the coordinator changes its location. While located in a new position, the virtual coordinator processes the information within the node spending the time ‫ݐ‬௦ in it which is defined by the user. The key step of an algorithm is the choice of the next position ‫݌‬௡ାଵ . Mainly, it is executed by using one of the heuristics methods. Different sources offer a variety of heuristics methods1, 5. One of the simplest methods is considered to be the random position selection from the list of possible positions:‫݌‬௡ାଵ ൌ ‫݉݋݀݊ܽݎ‬ሺܵሺ‫݌‬௡ ሻ. In a paper by one author5 it was offered to provide the movement of the coordinator along a visible network perimeter. This approach is based on the fact that most operating network elements within the network center have a lower amount of remaining power in the nodes. The proposed method (placing the coordinator node on the network perimeter) allows to load the nodes where the remaining amount of energy is higher. In his works Basagni proposes the usage of MRE (Maximum Residual Energy)1 coordinator motion algorithm. The coordinator node determines the remaining energy in the adjacent nodes through the GMRE algorithm and selects a single node with the largest remaining amount of energy in it. In this case the planning of the coordinator movements is linked to searching maximum remaining amount of energy in the nodes. There are several types of function ‫ܧ‬௞ which allow to determine the location of the coordinator k: ଵ

1. ‫ܧ‬௞ ሺ‫ݐ‬ሻ ൌ ȁ஽ሺ௞ሻȁ σ௝‫א‬஽ሺ௞ሻ ‫ܧ‬௥௝ ሺ‫ݐ‬ሻ;

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2. ‫ܧ‬௞ ሺ‫ݐ‬ሻ ൌ ݉݅݊௝‫א‬஽ሺ௞ሻ ‫ܧ‬௥௝ ሺ‫ݐ‬ሻ; 3. ‫ܧ‬௞ ሺ‫ݐ‬ሻ ൌ ݉ܽ‫ݔ‬௝‫א‬஽ሺ௞ሻ ‫ܧ‬௥௝ ሺ‫ݐ‬ሻ. Regardless of how the function ‫ܧ‬௞ is defined, the remaining amount of energy only partially characterizes the total state of the network and cannot be used as the sole condition. For example, a node with the smallest amount of remaining energy ‫ܧ‬଴ and the lowest consumption of power is able to function longer than a node with the highest ‫ܧ‬଴ and the highest energy consumption. The use of the MRE method only places the coordinator node in a position with the highest ‫ܧ‬଴ value and in case if the network management module does not facilitate a new topology change then this method will not increase the overall network life expectancy. The article offers an alternative method: in every operational step choose such a location ‫݌‬௡ of a virtual coordinator which allows the network to function as long as possible on the condition that the network topology will not be changed. Using an analogue the name of the power method MREML (Maximum Residual Energy Maximal Lifetime) is to be defined ‫ܧ‬௞ ሺ‫ݐ‬ሻ ൌ ܽ‫݃ݎ‬ሺ݉ܽ‫ݔ‬௞‫א‬ௌሺ௣೙ሻ ݉݅݊௡‫א‬஽ሺ௞ሻ

ாబ೙ ሺ௧ሻ ೙ ሺ௧ሻ ௣ೖ

ሻǡ

(11)

where: ‫݌‬௞௡ is the total power consumed in an operating cycle of the node k. The total power consumption of the network node can be calculated or changed dynamically based on external factors [3]. If the time which the coordinator spends in each of the node k positions equals ‫ݐ‬௞ , if the remaining amount of energy in the nodes is known before the coordinator is placed in a certain position ‫ܧ‬଴௡ ሺ‫ݐ‬ሻ and if the amount of energy is known when the coordinator will leave the position ‫ܧ‬଴௡ ሺ‫ ݐ‬൅ ‫ݐ‬௞ ሻ, then the power consumption of each node can be expressed by using a formula: ‫݌‬௞௡ ൌ

ாబ೙ ሺ௧ሻିாబ೙ ሺ௧ା௧ೖ ሻ ௧ೖ

.

(12)

MRE in contrast to the MREML method uses the network node local information about the remaining amount of energy that regularly requires additional power when analysing the remaining amount of the adjacent nodes. The proposed method in the article requires using a network node control module and the static coordinator node for storing information. This approach allows receiving a global overview of the entire network configuration if there is a change in the network topology; additionally, it allows the virtual coordinator node to take the best position for collecting the information from the nodes. 6. Conclusion The article proposes the management methodology of coordinator node mobility which allows increasing the life expectancy of the wireless sensor network by levelling the difference of the remaining energy between all network elements. To avoid unnecessary network topology changes, the article contains a set of conditions that determine the moment of changing the structure of the network. Based on technical data obtained from the sensor nodes within wireless sensor network operations as well as on the analysis performed by the structure of the network, the primary conditions for changing the topology have been determined: x The condition which depends on the location of the network coordinator; x The condition which depends on the operating segment of the network coordinator; x The condition which depends on the consumed energy imbalance in the network nodes. To reduce the energy consumption in the nodes, it is proposed to introduce control modules of autonomous network nodes which will monitor every position of the network nodes and will contribute to the network topology changes regardless of the functional forms of the node. As a result, an alternative low power radio receiver will be


used in order to provide communication and the receiver will transmit the technical information to the network node management module. In cases when the physical coordinator node cannot be reached or it is not possible to establish a communication route between the sensor node and the coordinator, it is offered to use a virtual coordinator which is managed by using heuristic algorithms. The developed management methodology of the coordinator node mobility allows balancing the amount of the remaining energy in the network nodes and increasing the total life expectancy of the system.

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Basagni S., Carosi A., Petrioli C. Controlled Vs. Uncontrolled Mobility in Wireless Sensor Networks: Some Performance Insights // Vehicular Technology Conference, 2007. VTC-2007 Fall. 2007 IEEE 66th. 2007. P. 269–273. Bi Y., Sun L., Ma J., Li N., Chen C., “HUMS: An Autonomous Moving Strategy for Mobile Sinks in Data-Gathering Sensor Networks,” EURASIP Journal on Wireless Communications and Networking, 2007, Article ID: 164574. Jurenoks A., Novickis L.. Wireless Sensor Network Live Circle Simulation Tools to Balance Energy Consumption in the Network Nodes. In: Proceedings of 29th European Simulation and Modelling Conference (ESM 2015), Leicester, UK, 2015. October, ISBN 978-9077381908, pp 245.- 249. Jurenoks A., Novickis L.. Wireless Sensor Networks LifeTime Assessment Model Development. In: Proceedings of 10th International Scientific and Practical Conference “Environment. Technology. Resources”, Rezekne, Latvia, 2015. June, ISSN 1691-5402, pp. 121 – 126. Mautz R., Tilch S. Survey of optical indoor positioning systems // Indoor Positioning and Indoor Navigation (IPIN), 2011 International Conference on. 2011. P. 1–7. Moghe R., Yang Yi, Lambert F., Divan D.. "Design of a low cost self powered “Stick-on” current and temperature wireless sensor for utility assets," in Energy Conversion Congress and Exposition (ECCE), 2010 IEEE, 2010, pp. 4453-4460. Raghunathan V., Schurgers C., Park S., Srivastava M., “Energy aware wireless microsensor networks”, IEEE Signal Processing Magazine, vol. 19, iss. 2, pp. 40–50, March 2002. Thai, M. T., Wang, F., Du, D. H., and Jia, X. 2008. Coverage Problems in Wireless Sensor Networks: Designs and Analysis. International Journal of Sensor Networks (IJSNET), Special issue on Coverage Problems in Sensor Networks 3, 3, 191-200. Vlajic N., Stevanovic D.. “Sink Mobility in Wireless Sensor Networks: A (Mis)Match between Theory and Pra- ctice,” Proceedings of the 2009 International Conference on Wireless Communications and Mobile Computing, Leip- zig, 12-14 October 2009. Voskov L., Panfilov P., Efremov S. et al. Universal wireless sensor networks technology platform and its applications // Proceedings of the 1st International Workshop on Networked embedded and control system technologies - NESTER. 2009. P. 127–131. Wang, Q. Source traffic modeling in wireless sensor networks for target tracking / Q. Wang, T.Zhang // In Proc. of the 5th ACM International Symposium on Performance Evaluation of Wireless Ad-Hoc, Sensor, and Ubiquitous Networks . - 2008. - 96-100 pp. Yunus, F. Proposed transport protocol for reliable data transfer in wireless sensor network (WSN) / F. Yunus, N.S.N.Ismail , S.H.S.Ariffin , A.A.Shahidan , N.Fisal , and S.K.Syed-Yusof // Proceedings of the 4th International Conference on Modeling, Simulation and Applied Optimization (ICMSAO), 2011. - 2011. - 1-7 pp. Жданов В. С. Проблемы и задачи проектирования беспроводных сенсорных сетей // Информационные, сетевые и телекоммуникационные технологии: сборник научных трудов, под ред. проф. д.т.н. Жданова В.С. 2009. С. 8–21.

Aleksejs Jurenoks is a lector at Riga Technical University. He obtained Master Degree in Computer Science in 2004 in Riga Technical University. He is the author of 14 publications and 6 educational books. He is regularly involved in different EU-funded projects: eLOGMAR-M (FP6, 2004-2006); IST4Balt (FP6, 2004-2007), UNITE (FP6, 2006-2008) and BONITA (INTERREG, 2008-2012). Michael Boronowsky is a Managing Director of the Innovation Capability Unit at the Center for Computing and Communication Technologies, University of Bremen. He studied Computer Science in Nijmegen (Netherlands) and received a Master Degree in Computer Science in 1995. Since this time he has been working at the University of Bremen. In the beginning he was working in the area of Artificial Intelligence research. In 1999 he became responsible for coordinating the Wearable Computing research at the TZI. Dr. Boronowsky finished his Doctoral studies in 2001 and became a Managing Director of the Intelligent Systems Department. Since 2002 he has been a Managing Director of the TZI and has become a Manager of the Innovation Capability Unit this year.

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