Self-Organizing Relay Network Supporting Remotely ... - IEEE Xplore

2014 6th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)

Self-Organizing Relay Network Supporting Remotely Deployed Sensor Nodes in Military Operations Dalimir Orfanus, Frank Eliassen

Edison Pignaton de Freitas

Department of Informatics University of Oslo Oslo, Norway [email protected], [email protected]

Electrical Engineering Department Federal University of Rio Grande do Sul Porto Alegre, Brazil [email protected] response is required, a usual requirement in military systems, this approach may not answer the needs.

Abstract—Data acquisition is an important task to provide context awareness of a military operation scenario in order to support informed decisions. Wireless sensor networks (WSN) play a remarkable service in this context by collecting data on remote areas to support decision support systems in control centers. However, due to the nature of the locations where they are deployed, it is out of the question provision a conventional communication infrastructure to access them. Thus, alternative communications approaches have to be used, such as relay links via unmanned aerial vehicles (UAVs) flying over the sensor network deployed on the ground. However, solutions based on this approach often propose intermittent connectivity to the WSN and/or exclusive use of the UAVs to support this connectivity. In this paper the problem is tackled by a self-organizing approach to control a UAV-relay network that provides persistent communication between WSN and back-end systems while the UAVs are not exclusively used for communication, but also have other missions to accomplish. Experimental results show the effectiveness of the proposed approach, which increased the connectivity among UAVs nearly by a factor of three compared to the deployment without any relay algorithm. Keywords—Self-organization; wireless communication relay; unmanned aerial connectivity

In the modern battlefield environment WSNs are just parts of larger operational networks, thus alternative solutions to provide them connectivity can be designed based on other elements that are part of the entire system. A potential candidate is surveillance devices. Considering area surveillance, the use of unmanned aerial vehicles (UAVs) is one of the key tools [4]. Joining these two pieces, interconnecting the sensors on the ground with the UAVs, a solution can be designed. The conception of this idea follows a trend in providing a new usage to retired unmanned combat aerial vehicles (UCAVs) as communications relay nodes as demonstrated in the DARPA’s Mobile Hotspot project [5]. The goal of this project is to provide troops on the ground with high-speed network connectivity during operations. However, differently from this refurbishment of the old UAVs employment, the proposal of this paper is to present a design in which UAVs currently in operation used for surveillance can also help as communication relays for WSNs deployed on the ground. One may argue that this is also possible to achieve using outdated UAVs that have been refurbished, but their core mission is to provide communication links, while the new ones have other higher priority missions and the harmonization between these two purposes of use is the goal of this work. The same comparison related to the dual usage purpose can be made to other mobile sink approaches to intermittently collect data from sensors on the ground, with the addition of that our new proposed approach provide persistent connection instead of intermittent one.

sensor networks; vehicles; network

I. INTRODUCTION Network centric warfare is an establish technology to support military operations [1]. Different types of networks are integrated in defense systems providing and consuming data in order to support the operations’ performance. Regarding data providers, wireless sensor networks (WSNs) are considered an important data source possibly deployable in any type of operational environment [2]. This is an important property of WSNs that can be exploited by military systems to deploy networks in harsh environments. However, due to the particular way they are deployed (usually air dropped) the sensor nodes tend to form groups which lack direct communication with one another as well as to the sink to report the acquired data. This problem can be solved by intermittent links provided by mobile sinks, also known as data mules, which, periodically or sporadically, collect their data sending them back to an overall sink node [3]. This solution may work for certain applications, but in cases in which permanent surveillance and immediate

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One of the key concepts of our approach is to be able to use different types of UAVs. On one hand, it is possible to directly use UAVs capable of long range communication to connect the sensors on the ground to back-end systems miles from the WSN deployed location. However, on the other hand it is also possible to take advantage of small UAVs performing area surveillance to connect with the ground sensors, and then to long range communication backbones, such as those provided by high altitude communicating Aerostats. This is a more general design that gives additional flexibility to the desired system. Our proposal also addresses another important issue: the UAVs are not exclusively dedicated to provide WSN

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2014 6th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)

connectivity, but they have their own priorities related to the surveillance missions they are accomplishing. Then, the issue is how to guarantee their mission accomplishment and at the same time the permanence of the communication link provided to the WSNs on the ground? In other words, providing the relay network is a supplemental activity besides the main military mission. To design a solution for this problem is a challenging task, composed of intricate logic and conditional behavior control spanning a huge range of possibilities depending on the possible variations in the environment and mission parameters. Considering the distributed nature of this problem and need for less (computationally) invasive solution that can co-exists with the primary mission, a decentralized approach is considered. It is not reasonable to consider a centralized one because of the inefficiency in tackling this type of problem [6]. This said, a promising path towards a solution may consider the application of the self-organizing (SO) paradigm. Self-organizing systems exhibit autonomic behavior with a high degree of robustness and at the same time they need only locally available information to apply it to small set of simple rules and local actions [7]. Interplay of these actions will give a rise to some more sophisticated system-level behavior. Such approach has been successfully applied in many areas [8].

spread over the region of interest to provide backbone links to the back-end networks that give access to the C2 systems. These links are implemented by long range broadband wireless technologies, based on e.g., the IEEE 802.16e standard. To reach the Aerostats, data acquired by the sparse WSN are relayed by a network of UAVs that provide persistent connectivity between the nodes on the ground and the Aerostats. The short (mid) range links can be implemented by IEEE 802.15.4 (extended range) or a variant of the IEEE 802.11, to name a few options. Figure 1 schematically presents the different elements of the proposed system.

II. OVERALL SYSTEM CONCEPTION AND APPLICATION WSNs are important data sources during military operations. They are able to support data acquisition to feed Command and control (C2) systems about the area under surveillance, providing situation awareness (SA) and thus supporting informed decisions. Despite this promising utility of WSNs, in vast operational scenarios, such as the Amazon environment in South America, it is impractical (useless and high cost) to deploy a WSN that completely covers large extensions such as the Amazon part of the Brazilian borderline. Anyhow, several area spots in this operational scenario are of great interest to stay under permanent surveillance. It is possible to observe this scenario as a WSN composed of several groups of nodes. These groups of nodes are disconnected from each other, and are responsible for covering specific locations. This means that the WSN is a partitioned network composed of “islands” of nodes. A possible solution to collect data from the isolated groups of sensor nodes is to have mobile sink nodes passing by them periodically or sporadically. Using such approach, the sensors would be waiting for a mobile sink for data collection, as discussed in [3]. This type of solution can also be considered as a Delay Tolerant Networks (DTN) approach [9], in which network connections and disconnections of controlled nodes, in this case the mobile sinks, are addressed. A DTN-based solution is relatively simple to implement and is able to retrieve data from sparse sensor nodes. However, the delays that the data transfer incurs due to the way approaches of these types work are not acceptable from the application point of view of defense systems. Thus, a permanent connection is required linking the sensors on the field to the back-end C2 systems.

Fig. 1. Schematic of the proposed system.

III.

SELF ORGANIZING UAV RELAY NETWORK

To present the proposed self-organizing relay network, we first describe the most important design principles of any selforganizing system. Then we describe the assumed military mission and mobility models used to accomplish it. The descriptions of the two proposed types of algorithms for data relay, reactive and proactive, are presented last. A. Basic principles of designing self-organizing behavior Because there is no direct relationship between local rules and actions with the system-level behavior, it is not trivial to properly design self-organizing systems. However there are several design methodologies (e.g. [10]) and design principles [11] that help a designer to tackle this challenge. Here we present only the essential ones. One of the key principles is to have processes in all interacting elements paired, i.e. they can exchange data and understand the common protocol. Another important design principle is to create positive and negative feedback. The positive feedback is responsible for the exploration and expansion part of the behavior while the negative one takes care of shaping the exploration to avoid system “explosion”. Both forces are applied at the same time creating dynamic balance.

The designed solution to address these needs consists of a heterogeneous network, in which the WSN of sparse grouped sensors is part of a larger network composed of Unmanned Aerial Vehicles (UAVs) and Aerostats. A few Aerostats are

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2014 6th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)

To recover lost links, we apply negative feedback that will force UAVs to get back closer to each other. This is done by returning the affected UAV to a place where there is a high chance to obtain connectivity again. It can for example be the position of the UAV received the most powerful signal within some time window. Or it can be the position of the sender of the most recently received signal.

B. Recoinnassance military mission One of the most commonly used military missions for UAVs is reconnaissance. The objective is to fully scan a given area with minimum time (or other cost metric). Due to the military use, there are certain constraints on UAVs moving patterns such as non-determinism. In other words, the flying path of each UAV should not be predictable. In this paper we consider the two most used mobility models applicable in this type of mission: pure and Markovian random walk. Both are based on randomness, thus satisfying requirement of hardly predictable flying path.

To detect connectivity we suggest a beacon based approach where the base station (sink for data collections) periodically sends a small beacon packet. This packet is then retransmitted (broadcasted) by all received UAVs. If the packet is not received before a timeout expires (time longer than a period plus maximum number of retransmissions), it is assumed that a given UAV has lost connectivity. To avoid flooding, each beacon has its sequence number (1 byte) and hop counter (1 byte). If a UAV receives a beacon with sequence number it has already retransmitted, the beacon is discarded. If the sequence number is new, then the number of hops is checked. In case it has reached the maximum, the packet is discarded, otherwise the counter is incremented. The beacon packet carries also a third field that contains the position of the last hop. This is used later when the link is lost to help the UAV to decide where to return back to. The size of the field depends on the required position accuracy. It is important to keep the size of the beacon packet as small as possible to minimize the probability of transmission collisions as well as to conserve the bandwidth. Pseudocode of the algorithm is presented in Listing 1.

In the pure random walk, each UAV picks a random direction (based on uniform distribution) and flies until: either a given time amount is reached, or a potential collision with another UAVs is detected. In the latter case, both UAVs turn to the right (or left) w.r.t. to their heading until clear of collision. If the border of the reconnaissance area is reached, a given UAV “bounces” back to the field. This mobility model produces very sharp paths including sharp turns backwards and has tendency to explore only small portions of an area [12]. However, due to its simplicity (stateless behavior), it is one of the most popular mobility models. For this reason we use it as a reference model. Improvement w.r.t. area coverage can be achieved via a modified Markov random model [12]-[13] The decision where to turn is still random, but it also takes the previous decision into account. The authors of [13] propose to use a decision table as show in TABLE I. This model has no mean direction and possible turn options are three discreet values (e.g. -45, 0, +45 degrees) that are selected according to corresponding probability in decision TABLE I. TABLE I.

01 02 03 04 05 06 07 08 09 10 11 12 13

DECISION TABLE WITH PROBABILITIES Current Decision

Previous Decisions

left

straight

right

left

70%

30%

0%

straight

10%

80%

10%

right

0%

30%

70%

fcn beacon_received( Beacon_pkt ) Timeout_timer.reset Neig_history.add( Beacon_pkt ) if( Beacon_pkt.hops < max_hops ) if( Beacon_pkt.seq_num not in Sequence_number ) Sequence_number.add (Beacon_pkt.seq_num) recent_seq