Utility-based sensor selection

In distributed source localization where sensors transmit measurements to a fusion node, we address the sensor selection problem where the goal is to find the best set of sensors that maximizes localization accuracy when quantization of sensor measurements is taken into account. Since sensor selection depends heavily upon rate assigned to each sensor, joint optimization of rate allocation and sensor selection is required to achieve the best solution. We show that this task could be accomplished by solving the problem of allocating rates to each sensor so as to minimize the error in estimating the position of a source. Then we solve this rate allocation problem by using the generalized BFOS algorithm. Our experiments demonstrate that the best set of sensors obtained from the proposed sensor selection algorithm leads to significant improvements in localization performance with respect to the set of sensors determined from a sensor selection process based on unquantized measurements.

Achieving optimal field coverage is a significant challenge in various sensor network applications. In some specific situations, the sensor field (target) may have coverage gaps due to the random deployment of sensors; hence, the optimized level of target coverage cannot be obtained. Given a set of sensors in the plane, the target coverage problem is to separate the sensor into different groups and provide them specific time intervals, so that the coverage lifetime can be maximized. Here, the constraint is that the network should be connected. Presently, target coverage problem is widely studied due to its lot of practical application in Wireless Sensor Network (WSN). This paper focuses on target coverage problem along with the minimum energy usage of the network so that the lifetime of the whole network can be increased. Since constructing a minimum connected target coverage problem is known to be NP-Complete, so several heuristics, as well as approximation algorithms, have been proposed. Here, we propose a heuristic for connected target coverage problem in WSN. We compare the performance of our heuristic with the existing heuristic, which states that our algorithm performs better than the existing algorithm for connected target coverage problem. Again, we have implemented the 2-connected target coverage properties for the network which provide fault tolerance as well as robustness to the network. So, we propose one algorithm which gives the target coverage along with 2-connectivity.

As sensors become more complex and prevalent, they present their own issues of cost effectiveness and timeliness. It becomes increasingly important to select sensor sets that provide the most information at the least cost and in the most timely and efficient manner. Two typical sensor selection problems appear in a wide range of applications. The first type involves selecting a sensor set that provides the maximum information gain within a budget limit. The other type involves selecting a sensor set that optimizes the tradeoff between information gain and cost. Unfortunately, both require extensive computations due to the exponential search space of sensor subsets. This paper proposes efficient sensor selection algorithms for solving both of these sensor selection problems. The relationships between the sensors and the hypotheses that the sensors aim to assess are modeled with Bayesian networks, and the information gain (benefit) of the sensors with respect to the hypotheses is evaluated by mutual information. We first prove that mutual information is a submodular function in a relaxed condition, which provides theoretical support for the proposed algorithms. For the budget-limit case, we introduce a greedy algorithm that has a constant factor of (1 - 1/e) guarantee to the optimal performance. A partitioning procedure is proposed to improve the computational efficiency of the algorithms by efficiently computing mutual information as well as reducing the search space. For the optimal-tradeoff case, a submodular-supermodular procedure is exploited in the proposed algorithm to choose the sensor set that achieves the optimal tradeoff between the benefit and cost in a polynomial-time complexity.

One ultimate goal of wireless sensor networks is to collect the sensed data from a set of sensors and transmit them to some sink node via a data gathering tree. In this work, we are interested in data aggregation, where the sink node wants to know the value for a certain function of all sensed data, such as minimum, maximum, average, and summation. Given a data aggregation tree, sensors receive messages from children periodically, merge them with its own packet, and send the new packet to its parent. The problem of finding an aggregation tree with the maximum lifetime has been proved to be NP-hard and can be generalized to finding a spanning tree with the minimum maximum vertex load, where the load of a vertex is a nondecreasing function of its degree in the tree. Although there is a rich body of research in those problems, they either fail to meet a theoretical bound or need high running time. In this paper, we develop a novel algorithm with provable performance bounds for the generalized problem. We show that the running time of our algorithm is in the order of O(mnα(m, n)), where m is the number of edges, n is the number of sensors, and α is the inverse Ackerman function. Though our work is motivated by applications in sensor networks, the proposed algorithm is general enough to handle a wide range of degree-oriented spanning tree problems, including bounded degree spanning tree problem and minimum degree spanning tree problem. When applied to these problems, it incurs a lower computational cost in comparison to existing methods. Simulation results validate our theoretical analysis.

The Wireless sensor network has a great applied prospect, so the military, academia and industry pay more and more extensive attention. The first, this article introduces the concept of sensor networks and development background; the second, compared with traditional networks, it summarizes the characteristics of sensor networks. In this basis, the application of the sensor network and the direction of the current study are a given brief introduction and a further discussion.

The Internet of Things comprises a large number of smart things bringing in physical world data that help resolve the challenges (e.g., Environmental pollution) we face every day. With the prevalence of IoT, some platforms emerged (e.g., Xively) on which people are allowed to share or retrieve sensor data and deploy applications. It is, however, a challenge to search/select appropriate sensors among enormous sensors for a particular application in an efficient and effective way. This paper investigates context-aware sensor selection, identification of sensors characteristics, and the process of publishing services. We propose a sensing service architecture with a sensor search and selection method to efficiently select relevant sensors among a large set of available sensors. The context-dependent parameters for sensor selection proposed in this paper include user requirements and context properties of sensors (e.g., Accuracy, sensing range, residual energy). Moreover, the proposed architecture attempts to reduce excessive network traffic caused by frequent change of sensor states. We develop a prototype of the proposed architecture with semantic sensor selection which can be applied to many applications. The prototype can select proper sensors intelligently for users according to their requirements to develop their personalized applications. Our simulation results show that the proposed search/selection method can achieve longer network lifetime, as compared with traditional keyword-based search methods.

Wireless sensor networks (WSNs) comprise nodes with restricted battery power that are deployed to observe some physical event within the sensor field. Information gathering is typical, but an important operation in several applications of WSNs. It is important to control the sensor network for longer period of time in an energy-efficient manner for gathering information. Hence, it is perpetually fascinating to design protocols that are energy efficient, have prolonged lifetime and can support event-driven applications. This paper proposes a protocol referred to as distance-based residual energy-efficient stable election protocol (DRESEP) that is nearly optimal for event-driven information gathering applications in sensor networks. The key idea of DRESEP is moving gathered information from sensor nodes to cluster head (CH) depending upon perceived changes. Dual-hop communication between CHs and base station is introduced to achieve energy minimization. Further, the results demonstrate that the proposed algorithm significantly outperforms existing algorithms in terms of energy optimization and system lifetime.

The Steiner tree problem asks for a minimum cost tree spanning a given set of terminals S⊂eqV in a weighted graph G = (V,E,c), c:E→ R+. In this paper we consider a generalization of the Steiner tree problem, so called Polymatroid Steiner Problem, in which a polymatroid P = P(V) is defined on V and the Steiner tree is required to span at least one base of P (in particular, there may be a single base S⊂eqV). This formulation is motivated by the following application in sensor networks – given a set of sensors S = {s1,…,s k }, each sensor s i can choose to monitor only a single target from a subset of targets X i , find minimum cost tree spanning a set of sensors capable of monitoring the set of all targets X = X1 ∪ … ∪ X k . The Polymatroid Steiner Problem generalizes many known Steiner tree problem formulations including the group and covering Steiner tree problems. We show that this problem can be solved with the polylogarithmic approximation ratio by a generalization of the combinatorial algorithm of Chekuri et al. (2002). We also define the Polymatroid directed Steiner problem which asks for a minimum cost arborescence connecting a given root to a base of a polymatroid P defined on the terminal set S. We show that this problem can be approximately solved by algorithms generalizing methods of Chekuri et al. (2002).

Wireless sensor networks consist of a large number of low-cost, low-power, and multifunctional sensor nodes that communicate over short distances through wireless links. Such sensor networks are ideal candidates for a wide range of applications such as monitoring of critical infrastructures, data acquisition in hazardous environments, industry control systems, vehicular networks, and military operations. Wireless sensor networks introduce new security challenges due to their dynamic topology, severe resource constraints, and absence of a trusted infrastructure. The purpose of this special issue is to publish high-quality research papers as well as review articles addressing recent advances of wireless sensor network security. In this special issue, we will explore the topic of security challenges and solutions for the wide application of wireless sensor networks in different areas, such as RFID and smart grid. RFID has been widely used in logistics and internet of things. Smart grid leverages sensor networks to better balance the load of the power grid. Both of them have great impact on the daily life and areweak points of thewhole economy,which attract the attention of terrorists. The feasible attacks and defense solutions are still not clear.The papers in this volume also cover several important foundational security services for wireless sensor networks, such as key management, key distribution, secure cluster formation, secure architecture for multihop communication, intrusion prediction, and secure localization. All these foundational security services are critical bases for application of wireless sensor networks. We hope that the papers in this volume engender further thinking about new security challenges for wireless sensor networks and even further “out-of-the-box” ideas about how to solve new security problems for wireless sensor networks.

Owing to numerous potential applications, wireless sensor networks have been the focus of a lot of research efforts lately. In this note we study one fundamental issue in such networks, namely the coverage problem, in which we would like to determine whether a region of interest is sufficiently covered by a given set of sensors. This problem is motivated by monitoring applications in sensor networks, as well as robustness concerns and protocol requirements. We show that the coverage problem and some of its variants can be treated in a unified manner using suitable generalizations of the Voronoi diagram. As a result, we are able to give algorithms that have better runtimes than those proposed in previous works (see, e.g., [5, 6]). Our approach also yields efficient algorithms for coverage problems where the sensing region of a sensor is an ellipse or an L p –ball, where p≥1.

In the paper, we propose a deterministic approximation algorithm for maximizing a generalized monotone submodular function subject to a matroid constraint. The function is generalized through a curvature parameter \(c\in [0,1]\), and essentially reduces to a submodular function when \(c=1\). Our algorithm employs the deterministic approximation devised by Buchbinder et al. [3] for the \(c=1\) case of the problem as a building block, and eventually attains an approximation ratio of \(\frac{1+g_c(x)+\Delta \cdot \left[ 3+c-(2+c)x-(1+c)g_c(x)\right] }{2+c+(1+c)(1-x)}\) for the curvature parameter \(c\in [0,1]\) and for a calibrating parameter that is any \(x\in [0,1]\). For \(c=1\), the ratio attains 0.5008 by setting \(x=0.9\), coinciding with the renowned performance guarantee of the problem. Moreover, when the submodular set function degenerates to a linear function, our generalized algorithm always produces optimum solutions and thus achieves an approximation ratio 1.

An unknown input observer provides perfect asymptotic tracking of the state of a system affected by unknown inputs. Such an observer exists (possibly requiring a delay in estimation) if and only if the system satisfies a property known as strong detectability. In this paper, we consider the problem of selecting (at design-time) a minimum cost subset of sensors from a given set in order to make a given system strongly detectable. We show that this problem is NP-hard even when the system is stable. Furthermore, we show that it is not possible to approximate the minimum cost within a factor of $\log n$ in polynomial-time (unless $P=NP$). However, we show that if a given system (with a selected set of sensors) is already strongly detectable, finding the smallest set of additional sensors to install in order to obtain a zero-delay observer can be done in polynomial time. Finally, we consider the problem of attacking a set of deployed sensors in order to remove the property of strong detectability. We show that finding the smallest number of sensors to remove is NP-hard.

The energy problem is one of the most important challenges in the application of sensor networks. With the development of wireless charging technology and intelligent mobile charger (MC), the energy problem can be solved by the wireless charging strategy. In the practical application of wireless rechargeable sensor networks (WRSNs), the energy consumption rate of nodes is dynamically changed due to many uncertainties, such as the death and different transmission tasks of sensor nodes. However, existing works focus on on-demand schemes, which not fully consider real-time global charging scheduling. In this article, a novel dynamic charging scheme (DCS) in WRSN based on the actor-critic reinforcement learning (ACRL) algorithm is proposed. In the ACRL, we introduce gated recurrent units (GRUs) to capture the relationships of charging actions in time sequence. Using the actor network with one GRU layer, we can pick up an optimal or near-optimal sensor node from candidates as the next charging target more quickly and speed up the training of the model. Meanwhile, we take the tour length and the number of dead nodes as the reward signal. Actor and critic networks are updated by the error criterion function of R and V. Compared with current on-demand charging scheduling algorithms, extensive simulations show that the proposed ACRL algorithm surpasses heuristic algorithms, such as the Greedy, DP, nearest job next with preemption, and TSCA in the average lifetime and tour length, especially against the size and complexity increasing of WRSNs.

Solving energy issues for sweep coverage in wireless sensor networks

Integration of distributed energy resources (DERs) into microgrid makes the power supply more reliable and reduces the cost. However, the connection of a large number of DERs among the load on middle-voltage/low-voltage feeders can result in severe voltage regulation problems. These challenges motivate the application of wireless sensor networks into microgrid. In this paper, several sensor selection schemes are heuristically proposed to improve the voltage measurement performance, prolong the sensor network lifetime, and guarantee the real-time communication between the distributed sensors and the intelligent control center. First, aiming to accurately monitor the real-time voltage level, we propose an opportunistic sensor selection scheme under equal power allocation and investigate the asymptotic behaviors of the voltage measurement performance. Furthermore, we address the sensor selection scheme under optimal power allocation and derive a reminiscent of “water-filling” solution. The proposed sensor selection schemes are applied and verified in the context of voltage regulation. In addition, we present the studies on the tradeoff between the voltage measurement and the sensor power consumption. Finally, we explore the joint power and spectrum allocation schemes to maximize the transmission rate between the sensors and the control center based on sensor selection. The theoretical analysis and proof are instrumental to the future wireless network design in microgrid.