Improved Approximation Algorithms for Connected Sensor Cover

In this paper, we consider the connected coverage problem and aim to construct a minimal connected cover set that is sufficient for a given query in wireless sensor networks. We propose a centralized, Voronoi tessellation (CVT) based algorithm to select the minimum number of active sensor nodes needed to cover the target region completely. The constructed sensor set proves to be connected when sensor node’s communication range is at least twice of its sensing range. For other situations where the CVT algorithm alone cannot guarantee the network connectivity, we design a Steiner minimum tree (SMT) based algorithm to ensure the network connectivity. Theoretical analysis and simulation results show that our algorithm outperforms the greedy algorithm in terms of both the time complexity and the needed number of sensor nodes that must be kept active to respond to a given query.

We study an online task assignment problem for multi-robot systems where robots can do multiple tasks during their mission and the tasks arrive dynamically in groups. Each robot can do at most one task from a group and the total number of tasks a robot can do is bounded by its limited battery life. There is a payoff for assigning each robot to a task and the objective is to maximize the total payoff. A special case, where each group has one task and each robot can do one task is the online maximum weighted bipartite matching problem (MWBMP). For online MWBMP, it is known that, under some assumptions on the payoffs, a greedy algorithm has a competitive ratio of 1 over 3. Our key result is to prove that for the general problem, under the same assumptions on the payoff as in MWBMP and an assumption on the number of tasks arising in each group, a repeated auction algorithm, where each group of tasks is (near) optimally allocated to the available group of robots has a guaranteed competitive ratio. We also prove that (a) without the assumptions on the payoffs, it is impossible to design an algorithm with any performance guarantee and (b) without the assumption on the task profile, the algorithms that can guarantee a feasible allocation (if one exists) have arbitrarily bad performance in the worst case. Additionally, we present simulation results depicting the average case performance of the repeated greedy auction algorithm.

Energy conservation is a very critical issue in WSN. A lot of work has been done on the techniques of topology control so that sensor nodes which are not in direct use can be put to a low power consuming state, thus saving energy. Recently improvements have been done in this field by considering individual sensors as individual cell in a grid and applying cellular automata principles to it. Our work uses Block cellular automata, thus considering a number of sensor nodes as a group or block. The grouping or clustering is performed by Grid clustering technique. The member nodes of a block or cluster will select a Cluster Head (CH), which will represent the block as a whole. The CA principles are applied here, by taking a whole block as a single unit and it will decide on the power mode of the CH. Other power conservation algorithms are applied within the cluster so as to increase the overall network lifetime.

This decision making process for a mobile robot can be adapted to clusters of sensor nodes rather than an individual sensor node. Preference can be given to a particular cluster for management (power management, node density management, etc.) based on activity level, the number of sensor nodes, and the importance of the activity. If the number of nodes is critical for the given application, then it gets a high weighting factor and would require some nodes to be put to sleep. We change the behavior of the network by tuning one of the parameters (increasing sleep time) based on the needs of the application. We intelligently analyzed the characteristic of a deployed sensor network for a given application and adaptively changed its operational behavior to suit the changing demands. We showed that appropriate sensor action needs to be scheduled in order to achieve maximum utility.

Cluster-based routing scheme is able to prolong the network lifetime and save the energy consumption. In this field, many cluster-based routing protocols are proposed such as LEACH, TEEN, APTEEN, and so on. On the other hand, the chain-based routing protocols construct the chain such as PEGASIS and concentric-clustering routing scheme. The concentric-clustering routing scheme is the multiple-chain among sensor node to transmit the data to the base station or lower cluster head. However, a problem of this scheme is that the number of sensor nodes in the each level is unbalanced as assigning the level based on total number of level. Thus, the unbalanced energy consumption among sensor nodes is occurred. Also, as this scheme selects the next cluster head to be closely located at current cluster head, energy of cluster head dissipates quickly on the multipath among cluster heads. To overcome these problems, we propose the advanced concentric-clustering routing scheme adapted to large-scale sensor networks. Our scheme fixes the number of sensor node on level for the same number of sensor nodes in each level. In addition, we consider the residual energy as the criterion of cluster head election for balanced energy consumption as well as the distance from lower cluster head or base station to upper cluster head. As the simulation results, our scheme shows a better performance of energy consumption than other multiple-chaining schemes about 24.4%.

Fault-tolerant monitor placement for out-of-band wireless sensor network monitoring

Wireless sensor network (WSN) TestBed design challenges can be divided into three main sub-categories: architectural, hardware and software. By considering all these challenges, an optimal solution was found and EDI TestBed created. In this paper, a method, which solves software related problems in large scale WSN TestBed, such as how to distribute computational resources, control all of the TestBed devices and how to efficiently acquire and structure the data, is described. The experimental test results shows that proposed method is advantageous for large scale TestBeds (more than 20 sensor nodes), and its efficiency increases rapidly if the number of sensor nodes in TestBed increases.

We investigate the secure connectivity of wireless sensor networks under the pairwise key distribution scheme of Chan et al.. Unlike recent work which was carried out under the assumption of full visibility, here we assume a (simplified) communication model where unreliable wireless links are represented as on/off channels. We present conditions on how to scale the model parameters so that the network i) has no secure node which is isolated, and ii) is securely connected, both with high probability when the number of sensor nodes becomes large. The results are given in the form of zero-one laws, and exhibit significant differences with corresponding results in the full visibility case.

A Connected Dominating Set (CDS) of a graph representing a Wireless Sensor Network can be used as a virtual backbone for routing through the network. Since the sensors in the network are constrained by limited battery life, we desire a minimal CDS for the network, a known NP-hard problem. In this paper we present three randomized algorithms for constructing a CDS. We evaluate our algorithms using simulations and compare them to the two-hop K2 algorithm and two other greedy algorithms from the literature. After pruning, the randomized algorithms construct a CDS that are generally equivalent in size to those constructed by K2 while being asymptotically better in time and message complexity. This shows the potential of significant energy savings in using a randomized approach as a result of the reduced complexity.

This chapter is devoted to the study of 16 types of greedy algorithms for decision tree construction. The dynamic programming approach is used for construction of optimal decision trees. Optimization is performed relative to minimal values of average depth, depth, number of nodes, number of terminal nodes, and number of nonterminal nodes of decision trees. We compare average depth, depth, number of nodes, number of terminal nodes and number of nonterminal nodes of constructed trees with minimum values of the considered parameters obtained based on a dynamic programming approach. We report experiments performed on data sets from UCI ML Repository and randomly generated binary decision tables. As a result, for depth, average depth, and number of nodes we propose a number of good heuristics.

Wireless sensor networks (WSNs) are used to collect large amounts of data over a wide area. However, the sensor nodes used in WSNs have a short lifespan because they are generally battery-operated. Research is actively underway to increase the lifespan of WSNs via techniques such as energy harvesting and wireless power transfer (WPT). This study proposes a scheme for clustering and adjusting the WSN data collection rate to alleviate the hotspot problem and improve network connectivity. The proposed scheme is targeted at WSNs in which the sensor nodes harvest solar energy. In addition, an unmanned aerial vehicle (UAV) traverses along a given path and delivers energy to sensor nodes using WPT, while collecting data as a sink node. In this scheme, the number of cluster heads is determined by considering the number of nodes at each hop distance and the maximum amount of data that can be transferred with the available energy. Further, sensor nodes limit the amount of data they collect to reduce the relay load on intermediate nodes. To achieve this, nodes consider the amount of data transferred by parent nodes, especially nodes in the hotspot, and the number of child nodes. The sink node traverses along a given path, and collects the data accumulated in the cluster head while supplying its remaining energy to the cluster head. The amount of energy it transfers is determined by considering the number of cluster heads. Consequently, nodes in the hotspot are prevented from becoming blacked out from a lack of energy, and the energy harvesting efficiency is increased. Simulation results indicate that the proposed scheme reduces the number of blackout nodes in the hotspot areas near cluster heads. Furthermore, the data collection and monitoring performance increases with the increase in network connectivity.

SummaryIn wireless sensor network, a large number of sensor nodes are distributed to cover a certain area. Sensor node is little in size with restricted processing power, memory, and limited battery life. Because of restricted battery power, wireless sensor network needs to broaden the system lifetime by reducing the energy consumption. A clustering‐based protocols adapt the use of energy by giving a balance to all nodes to become a cluster head. In this paper, we concentrate on a recent hierarchical routing protocols, which are depending on LEACH protocol to enhance its performance and increase the lifetime of wireless sensor network. So our enhanced protocol called Node Ranked–LEACH is proposed. Our proposed protocol improves the total network lifetime based on node rank algorithm. Node rank algorithm depends on both path cost and number of links between nodes to select the cluster head of each cluster. This enhancement reflects the real weight of specific node to success and can be represented as a cluster head. The proposed algorithm overcomes the random process selection, which leads to unexpected fail for some cluster heads in other LEACH versions, and it gives a good performance in the network lifetime and energy consumption comparing with previous version of LEACH protocols.

Wireless sensor networks become popular due to its attracting applications like monitoring and tracking. A sensor network is composed of a large number of sensor nodes, which are densely deployed and relay on limited battery power. In an Ad-Hoc network better Quality of Services can be achieved either by using high transmit power or by reducing number of hops. Transmitting at unnecessarily high power reduces the life time of sensor node as well as network. One of the goals of forming a network is to have power optimization-that is, each sensor node should be able to communicate with any other sensor node via less number of hops. Involving less number of intermediate sensor nodes in a route provides better QoS as well as rest sensor nodes can be involved in other transmission. The strategy of using optimum number of hop in multi hop communication is to provide less number of transmission in a route, which provides less involvement of semiconductor noise, signal interference, fading, scattering and shadowing, which ultimately improves QoS and also preserve the power of other sensor nodes.

Internet of Things (IoT) invoke to linkage of individually identifiable embedded devices within the current internet infrastructure. Wireless Sensor Network (WSN) is a network containing self-ruling sensors, which monitors environmental conditions. Interconnection of these sensors into IoT will be a big revolution in growing sensor technology. The increase in the number of sensor nodes, increases the number of faulty nodes. This affects the Quality of Service (QoS) of WSN based IoT. Efficient detection and reuse of faulty nodes enhances the quality of monitoring to a large extend. Due to the difficulty in indentifying the internal status of sensor nodes, it is important to develop algorithms to find faulty nodes. The conventional fault detection algorithms face low detection accuracy. In this work, three input fuzzy inference system (FIS) is used, which identifies hardware faults like, transmitter circuit condition, receiver circuit condition and battery condition. The simulation results show that the proposed scheme increases the detection accuracy when compared to the conventional schemes.

In this paper, a novelself-localizationprocedure for wireless sensor networks is accessible.Due to errors in calculatinguniversal coordinates andinappropriate relative pairwise distance assessment, the errors in the appraisal of localization mayhappen.Minimization of these errors is necessary for effectivelocalization arrangements.In irregular or sparse networks, the earlier proposed DV-Hop positioning procedureshows poor accuracy and not effective.To avoidthe weaknesses of this procedure,in this work anenhanced DV-Hop procedure for self-localization (EDVHPSL) is proposed.The anticipatedEDVHPSLmethoddelivers improvement in results in relations of localization error.MATLAB simulations have been performed to implement the proposed EDVHPSLby changing the benchmark parameters during simulation process.The parameters are number of beacon nodes, range, number of sensor nodes and hop count.