Haut PDF Opportunistic data collection and routing in segmented wireless sensor networks

Opportunistic data collection and routing in segmented wireless sensor networks

Opportunistic data collection and routing in segmented wireless sensor networks

Abstract The regular monitoring of operations in both movement areas (taxiways and runways) and non-movement areas (aprons and aircraft parking spots) of an airport, is a critical task for its functioning. The set of strategies used for this purpose include the measurement of environmental variables, the identification of foreign object debris (FOD), and the record of statistics of usage for diverse sections of the surface. According to a group of airport managers and controllers interviewed by us, the wide monitoring of most of these variables is a privilege of big airports due to the high acquisition, installation and maintenance costs of most common technologies. Due to this limitation, smaller airports often limit themselves to the monitoring of environmental variables at some few spatial points and the tracking of FOD performed by humans. This last activity requires stopping the functioning of the runways while the inspection is conducted. In this thesis, we propose an alternative solution based on Wireless Sensor Network (WSN) which, unlike the other methods/technologies, combines the desirable properties of low installation and maintenance cost, scalability and ability to perform measurements without interfering with the regular functioning of the airport. Due to the large extension of an airport and the difficulty of placing sensors over transit areas, the WSN might result segmented into a collection of subnetworks isolated from each other and from the sink. To overcome this problem, our proposal relies on a special type of node called Mobile Ubiquitous LAN Extension (MULE), able to move over the airport surface, gather data from the subnetworks along its way and eventually transfer it to the sink. One of the main demands for the deployment of any new system in an airport is that it must have little or no interference with the regular operations. This is why the use of an opportunistic approach for the transfer of data from the subnetworks to the MULE is favored in this thesis. By opportunistic we mean that the role of MULE will be played by some of the typical vehicles already existing in an airport doing their normal displacements, and the subnetworks will exploit any moment of contact with them to forward data to the sink. A particular characteristic of the MULEs in our application is that they move along predefined structured trajectories (given by the layout of the airport), having eventual contact with the set of nodes located by the side of the road (so-called subsinks). This implies the need for a data routing strategy to be used within each subnetwork, able to lead the collected data from the sensor nodes to the subsinks and distribute the data packets among them so that the time in contact with the MULE is used as efficiently as possible. In this thesis, we propose a routing protocol which undertakes this task.
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Efficient data aggregation and routing in wireless sensor networks

Efficient data aggregation and routing in wireless sensor networks

Abstract Wireless Sensor Networks (WSNs) have gained much attention in a large range of technical fields such as industrial, military, environmen- tal monitoring etc. Sensors are powered by batteries, which are not easy to replace in harsh environments. The energy stored by each sensor is the greatest impediment for increasing WSN lifetime, be- cause power failure of a sensor not only affects the sensor itself, but also its ability to forward packets on behalf of others sensors. Since data transmission consumes more energy than sensing and processing activities, our major concern is how to efficiently transmit the data from all sensors towards a sink. We address this issue by proposing a global solution addressing aggregation, routing as well as channel as- signment. We suggest three tree-based data aggregation algorithms: Depth-First Search Aggregation (DFSA), Flooding Aggregation (FA) and Well-Connected Dominating Set Aggregation (WCDSA) to re- duce the number of transmissions from each sensor towards the sink. In each proposed algorithm, the degree of connectivity of each sen- sor is taken into account in the tree construction, by electing sensors having the highest degree of connectivity as parents, and sensors with the lowest as leaves. As a result, aggregated data is efficiently trans- mitted along the shortest path through multiple hops from parent to parent towards the sink, helping to reduce the number of individual transmissions. Our approach provides local optimization for energy saving that can be used in dense configurations.
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Random Walk Based Routing Protocol for Wireless Sensor Networks

Random Walk Based Routing Protocol for Wireless Sensor Networks

Many earlier recent research efforts have raised this vision by focusing primarily on basic properties of random walks. For example, in [14] the authors addressed the problem of data gathering in large-scale WSNs with static sensor nodes and one mobile collector node that performs a random walk on a square lattice. Whenever the collector node enters the transmission range of a sensor node, the data are collected. In this context, the authors derived analytical bounds for the expected number of distinct visited sensor nodes within a given time frame. To improve this performance metric, they proposed a practical algorithm that constrains the ran- dom walk and validated it by simulations. Constrained ran- dom walk techniques, already suggested in [19] for multi- path routing, have the advantage to achieve load balancing property in uncontrolled dynamics characterized by random ON-OFF transitions to save energy. Besides the load bal- ancing property, which is difficult to achieve for other rout- ing protocols, it is also proven in [20] that a random walk based routing in regular patterned WSNs consumes the same amount of energy as the shortest path routing provided that messages are of small size, which characterizes many WSN applications.
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RTXP: A Localized Real-Time MAC-Routing Protocol for Wireless Sensor Networks

RTXP: A Localized Real-Time MAC-Routing Protocol for Wireless Sensor Networks

PEDAMACS [19] also uses a scheduled approach, but with only one radio channel. Nodes have different transmission powers. The sink can reach all the nodes in the network. The other nodes have two transmission powers: one to com- municate and one to identify their interferers. The proto- col needs a global synchronization of the network. This is achieved thanks to synchronization packets that are sent by the sink to the whole network. The protocol consists of three phases. In the first one, the topology learning phase, each node learns its interferers and neighbors by sending hello packets in contention periods. During the second phase, the topology collection phase, the information is sent to the sink using a contention mechanism. A schedule is computed by the sink and sent to the nodes. The method used to produce the schedule is to linearize the graph of the network (contain- ing the interference edges) and to give the same color to non interfering levels. The slots are allocated to non-interfering sets of nodes with the same color. During the third phase, the nodes communicate in their allocated slots. RT-Link [29] uses a similar scheme: a global schedule is produced by the sink. Nevertheless, RT-Link uses a 2-hop heuristic in- stead of determining real interferers as in PEDAMACS. In RT-Link, the nodes are synchronized with an out of band scheme based on dedicated hardware added to the sensor nodes. We can notice that, unlike PEDAMACS, in RT- Link, CSMA/CA access slots allow to add new nodes in the schedule during the run-time of the protocol.
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A cross-layer MAC and routing protocol based on slotted Aloha for Wireless Sensor Networks

A cross-layer MAC and routing protocol based on slotted Aloha for Wireless Sensor Networks

In this paper, we use the cross-layer approach to design a new protocol, PLOSA (Path-loss Ordered Slotted Aloha protocol), for WDCN. PLOSA modies frame aloha to reduce energy consumption. The frame aloha protocol is a widely used access protocol that is characterized by its simplicity, establishing itself as a good candidate for WDCN. However the price of its simplicity is a lack of fairness in media access. Nodes are at various distances from the collector. In free space propagation model, signal attenuation is strictly related to the distance between the transmitter and the receiver. The received signal strength of distant nodes is signicantly lower than those of close nodes. Due to the capture eect, distant nodes have a lower throughput than close nodes. In WSN, the utilization of a multi-hop mechanism avoids the capture eect. PLOSA proposes a multi-hop cross-layer routing protocol where the idea is to order the access of nodes to optimize the energy consumption. The transmission channel is divided into dierent slots and a node has access to a slot related to its distance. The higher the distance between a node and the collector, the earlier this one can access a slot. Once the access of nodes is ordered, the resulting routing protocol is very simple. Indeed it does not require the notion of routing table (the next forwarding hop is always closer to the collector). Our protocol reduces at a minimum the overhead in both the routing protocol and the collision avoidance mechanism. No routing information are required to nd a path between a sensor and the collector. Each time a node sends a packet, a closer one to the collector forwards it until it reaches the collector. In the same way, the number of collisions is limited as the access of nodes is ordered. A collision can only occur in the vicinity of a sender node i.e. two nodes can send a packet into the same time slot if they are at the same distance from the collector. Hence our protocol avoids the hidden node problem without the use of an intrusive collision avoidance mechanism as RTS/CTS handshake. To our knowledge, no other cross-layer routing protocol exists addressing the question of how avoiding routing overhead and hidden node problem. Indeed PLOSA protocol is designed to oer high delivery rate and low end-to-end delay. In most cases PLOSA provides data delivery to the collector within one frame.
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Adaptive Reliable Routing Protocol for Wireless Sensor Networks

Adaptive Reliable Routing Protocol for Wireless Sensor Networks

madjid.bouabdallah@hds.utc.fr Abstract —Many Wireless Sensor Networks (WSN) applications success is contingent upon the reliable delivery of high-priority events from many scattered sensors to one or more sink nodes. In particular, WSN has to be self-adaptive and resilient to errors by providing efficient mechanisms for information distribution especially in the multi-hop scenario. To meet the stringent requirement of reliably transmitting data, we propose a lightweight and energy-efficient joint mechanism for packet loss recovery and route quality awareness in WSNs. In this protocol, we use the overhearing feature characterizing the wireless channels as an implicit acknowledgment (ACK) mechanism. In addition, the protocol allows for an adaptive selection of the routing path, based on a collective cooperation within neighborhood.
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A Cross-Layer Medium Access Control and Routing Protocol for Wireless Sensor Networks

A Cross-Layer Medium Access Control and Routing Protocol for Wireless Sensor Networks

C. Frame Exchange Sequence In Fig. 1, as an example, an event occurs in the environment and triggers a data transmission by node 1. If the medium is determined to be idle for a period of Distributed Interframe Spacing (DIFS) time and its NAV is equal to zero, node 1 broadcasts an RTS frame to all its neighbors. The structure of an RTS frame is given in Fig. 2. A CTS_Wait timer is set in node 1 to wait for a CTS frame response from potential next- hop nodes that will be competing in the contention process. The sensor nodes that receive the RTS frame such as nodes 2, 3, 4 and 41 in Fig. 1 then compare their RSSI levels with node 1’s RSSI value. In this figure, nodes 2 and 3 are assumed to have slightly higher received power levels than node 1, and hence they participate in the contention process. Nodes 2, 3 and 4 independently sets a CTS_Response timer, defines as a corresponding amount of time that must elapse before replying a CTS frame to node 1 as shown in Fig. 3. The amount of time that must elapse for each of these next-hop nodes depends on the random timeslots chosen between 0 and a fixed maximum contention window size (
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PiRAT: Pivot Routing for Alarm Transmission in Wireless Sensor Networks

PiRAT: Pivot Routing for Alarm Transmission in Wireless Sensor Networks

Complexe scientifique des C´ezeaux, 63177 Aubi`ere cedex, France Emails: {nancy,guitton,misson}@sancy.univ-bpclermont.fr, bakhache@hotmail.com Abstract—Wireless sensor networks are increasingly used for remote monitoring, fire detection, emergency response. Such networks are equipped with small devices powered by batteries and designed to be operated for years. They are often based on the ZigBee standard which defines low power and low data rate protocols. As network size and data rates increase, congestion arises as a problem in these networks, especially when an emergency situation generates alarm messages in a specific area in the network. Indeed, congestion occurs as the alarms converge to a specific destination, which results into packet losses and higher delays. In this paper, we propose a solution for congested links, called the PiRAT (Pivot Routing for Alarm Transmission) protocol. It is based on multi-path routing in order to add some diversity in routing the alarms. PiRAT uses intermediate nodes as pivots to reach the destination. Simulation results show that PiRAT has better performance than previous protocols in terms of packet loss, end-to-end delay, congestion and node overload.
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Dependable Routing Protocol Considering the k-Coverage Problem for Wireless Sensor Networks

Dependable Routing Protocol Considering the k-Coverage Problem for Wireless Sensor Networks

Keywords-Fault tolerance; Sensor Networks; k-coverage; Routing; Protection; I. I NTRODUCTION Wireless sensor networks (WSNs) are generally deployed to monitor areas and provide measurements for surveillance applications. WSNs have several application domains: to monitor the environment/habitat, to collect information, to register and process environmental parameters for optimiza- tion or prediction, and/or to insure security ([2] describes many applications and challenges). Often, the measurement or surveillance task of a WSN requires the complete cover- age of a target area or a set of target objects. In a general WSN architecture, several sensor nodes send the observation data to Base Stations (BSs) or sinks. Then data can be processed by the sinks and later send to the potential clients. The sensors are performing sensing and communication tasks and the main problems and challenges of this kind of networks are associated to these two activities [1]. The sensor network should be capable to measure in the target area and to process the measured values and transmit them to sink nodes. As several critical applications can depend on the measurement results, reliability of the overall network is a key issue, including both measurement reliability (which often requires multiple nodes to measure the same area) and communication (supported by routing).
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On the data delivery delay taken by random walks in wireless sensor networks

On the data delivery delay taken by random walks in wireless sensor networks

walks to convey data from a source node to a destination one. The use of this technique is not new and has been extensively explored in many networking models providing a variety of algorithms including routing [25], self-stabilization [8], data gathering and query processing in wireless networks [2], [19], [23], peer-to-peer networks [14] and other distributed systems. However, throughout the variety of research works that assess the effectiveness of random walk techniques, most results are derived from a qualitative view or by means of simulations [25]. Furthermore when analytical tools are used, the obtained results often provide bounds on various perfor- mance metrics of interest [3], [22]. For example, different authors are interested in the well studied concept of cover time, which is the expected time taken by a random walk to visit every node in a graph. This property is relevant to a wide range of algorithmic applications [2], [6], and various methods of bounding the cover time of graphs have been thoroughly investigated [10], [16]. Recently, it has been proven that for any size- n geometric graph with connectivity radius r, when r = Θ(r con ) 1 then w.h.p. 2 the optimal cover time behaves as
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Biased Random Walk Model to estimate Routing Performance in Wireless Sensor Networks

Biased Random Walk Model to estimate Routing Performance in Wireless Sensor Networks

border X = 0 until the nearness from the axis X = N takes away and therefore, the mean data gathering delay goes down again. 4 Conclusion In this paper we have related quantitatively the degree of knowledge to the routing performance and we have studied to what extend the state information available at network nodes can be minimized to reduce the complexity while ensuring an efficient routing scheme. This paradigm arises especially in the design of WSN where the localized approach is extensively embraced. With the aid of random walk theory, we have confirmed analytically the intuitive result that the larger the amount of state information, the more efficient the routing scheme. All details of this model will appear in the full version of this paper.
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Energy efficieny routing protocols for wireless sensor networks : Contribution to lifetime maximisation

Energy efficieny routing protocols for wireless sensor networks : Contribution to lifetime maximisation

Schurgers et al. [C. Schurgers, 2001] proposed another variant of directed diffusion, called Gradient-Based Routing (GBR). The key idea behind GBR is to keep the number of hops when the interest is diffused through the whole network. Which means that each node can calculate a parameter called the height of the node. This parameter is the minimum number of hops to attain the base station. The gradient on the link between a node and its neighbor is defined as the difference between a node height and that of its neighbor. So that, a packet is forwarded on a link with the largest gradient. GBR protocol uses some additional techniques such as data fusion and traffic spreading in order to uniformly balance the traffic over the network. When multiple paths pass through a node, which acts as a relay node, that relay node may aggregate data according to a certain function. In GBR, three different data dissemination techniques have been proposed (1) Stochastic Scheme, where a node chooses one gradient at random when there are two or more next hops that have the same gradient, (2) Energy-based scheme, where a node increases its height when its energy falls below a certain threshold, so that other sensors are prevented from sending data to that node, and (3) Stream-based scheme, where new streams are not relayed through nodes that are currently part of the path of other streams. The main objective of these schemes is to achieve a balanced distribution of the traffic in the network, which allows extending the network lifetime.
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Data collection of mobile sensor networks by drones

Data collection of mobile sensor networks by drones

6.1 Conclusion The goal of this thesis was to improve the efficiency of data collection in UAV-assisted mobile wireless sensor networks. Since the data-rate and contact duration time between the collectors and the source nodes are the main factors influencing data collection, we focused on the two factors and started by enhancing the contention- free algorithms in Chapter 3 . At the beginning of each time slot, the UAV sends a beacon to its coverage, the sensors that received the beacon send a join message which is including the details (such as position, speed, etc.) to the UAV. After having received the join message, the UAV processes these data and decides which sensor will be allocated to the current time slot according to the proposed algorithms. The sensors only send data in their time slots. Based on the proposed algorithms, the time slots are always allocated to the nodes that have the advantage of optimal transmission. For example, the node which has higher data-rate gets the time slot than the one who has lower data-rate according to the DR algorithm (in Chapter 3.3 ). Extensive simulations presented that the proposed algorithms achieve a high delivery ratio (sometimes, more than 95%, which shown in Chapter 3.4 ) in such context.
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Load repartition for congestion control in multimedia wireless sensor networks with multipath routing

Load repartition for congestion control in multimedia wireless sensor networks with multipath routing

The load repartition strategies vary from the simplest one which distributes uniformly the traffic on all available paths simultaneously to more complex strategies with explicit con- gestion notifications (CN) from congested nodes towards the sources. In these cases, on reception of a CN, a source will try to balance its traffic on available paths in order to keep its sending rate unchanged while reducing the amount of data sent on the current active paths. Congestion inferences could be based on the queue length at intermediary nodes such as in CODA or ESRT. At this point, we must state that the proposed solutions does not seek to obtain the optimal load repartition on all existing paths, but rather to react as quickly as possible to congestion to avoid packet losses in very resource-constrained devices. Therefore we plaid for a simple mechanism that limits both the number of exchanged control messages and the complexity at the sources.
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A Location Routing Protocol Based on Smart Antennas for Wireless Sensor Networks

A Location Routing Protocol Based on Smart Antennas for Wireless Sensor Networks

route error message if upon reception of a data packet the next hop on the route is broken. As soon as the source node gets the route error message, it triggers a route discovery for destination D, using the limited flooding scenario. To be able of determining whether the next hop on the route is working properly or not, every node will send periodic hello messages, with frequency hello_time, to the nodes that appear in its routing table as pre-hop (i.e. predecessors), only in Active routes; the neighbors that receive this packet keep record of the connectivity information. Failing to receive max_hello_loss consecutive hello message is an indication that the next hop is out of order and therefore, in the event a data packet must be transmitted to it, a route error message will be generated in return. Having described the propagation method used to flood the network in either scenario available for the RD phase, in the following section we will discuss the route request message in detail. 3.3.1.1 Route Request
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Low overhead loop-free routing in wireless sensor networks

Low overhead loop-free routing in wireless sensor networks

A. RPL Datapath Validation In RPL [1], routing loops are not possible because every data packet bears a RPL option header containing the direction of the packet (bit O) and the rank of the sender. In this way, every packet sent contributes to checking the topology—this mechanism is inherited (like trickle) from the Collection Tree Protocol (CTP) [2]. Unfortunately, this property comes at a rather high price: for the packets originated from outside of the sensor network or for the packets destined to outside, it becomes mandatory to tunnel them so to add an outer IPv6 header. In this way, in case a routing problem is detected for inward traffic, the ER gets the ICMP notification. Strong packet header compression is mandatory to regain a satisfactory efficiency with such over- head. Interestingly, the piggybacking of routing information on each packet was never questioned during the elaboration of RPL.
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Improving Decision-Making for Fuzzy Logic-based Routing in Wireless Sensor Networks

Improving Decision-Making for Fuzzy Logic-based Routing in Wireless Sensor Networks

Abstract—The task of routing data from a source node to the base station is a critical issue in Wireless Sensor Networks (WSNs). Fuzzy logic is the main proposal of a number of papers in the literature as an effective method for making decisions to transfer data towards the destination. Although fuzzy logic has a very important role in designing routing protocols for WSNs, identifying its fuzzy sets and defining best possible rules is a complex challenge. This paper introduces Improved-fuzzy logic (I-fuzzy), a simple and effective method that helps to address the weakness of fuzzy logic in terms of defining rules. The I- fuzzy method is tested in several scenarios by using GloMosim simulator and compared to a classic fuzzy logic approach and to a traditional minimum hop routing. The results show that the I-Fuzzy method outperforms the other approaches in terms of data delivery, energy conservation and load distribution.
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Vehicle routing problem for information collection in wireless networks

Vehicle routing problem for information collection in wireless networks

C j + kr j , if station j has not been visited before time point k, (k − t last )r j , otherwise, where t last is the time of the last extraction. Only the base station is appropriately equipped for sending information outside the network. A unique vehicle is in charge of collecting data from all the sta- tions in V \ {1} and of transporting it to the base sta- tion. There is no capacity limit associated to the vehi- cle. At the beginning of the time horizon, the vehicle is located at the base station and at the end of the time horizon, it must return to the base station. Multiple visits are allowed to each node in V . Information can only be transmitted when the vehicle is located in one of the stations in V , i.e., no transmission is allowed while the vehicle is moving on an arc (i, j) ∈ A. We also assume that, once a station i starts a transmission to the vehicle, all the current information located in i at that moment must be transmitted.
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Data collection and management solution for wireless sensor networks

Data collection and management solution for wireless sensor networks

Correspondent author: gil.de-sousa@cemagref.fr Abstract Wireless sensors networks (WSN) use can be very interesting in agricultural and environmental data collection. The first WSN generations operated in a continuous data stream mode which generates high energy consumption. This article presents a new WSN platform that limits data exchanges and has an increased lifetime. All of its components are designed in a resource aware mode in relation with energy, memory and processing. This platform is built on wireless sensors, implementing a hardware component-based concept, that allow them to be combined to form a more evolved wireless device. To manage this wireless sensor, a hybrid operating system, both multithreading and based on events, has been developed and is associated to a micro-file system. A WSN management tool is in charge of monitoring the wireless sensors and of the data collection. A first evaluation of this WSN platform has been realised in an agricultural context data collection.
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Fairness-Aware UAV-Assisted Data Collection in Mobile Wireless Sensor Networks

Fairness-Aware UAV-Assisted Data Collection in Mobile Wireless Sensor Networks

The main role of MSKs is to gather data from static sensor nodes. MSKs could be classified into mobile collectors and mobile relay nodes, according to its role in WSNs. Maximum Amount Shortest Path (MASP) [10] was proposed for a dy- namic network with MSK as a mobile collector in the sensing path. MASP scheme divided the sensing path into two parts: MCA (Multi hop Communication Area) and DCA (Direct Communication Area). One part is for communicating directly, and another one is for sub-sink. The MSK identifies the static nodes that are within its communication range: either sub- sinks or communicating static nodes and the MSK collects data only from sub-sinks. Jain et. al [6] provide a data collection algorithm that apply the middle node as a relay node, in their three tier scenario. The upper node is the destination node. The relay node is responsible for collecting information from the lower node and forward them. However, they are mostly concentrate on static networks.
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