In smart grid, Advanced Metering Infrastructure (AMI) systems feed the metering data to Energy Supplier (ES) every 15 minutes. Using this information, ES can offer incentives to reduce the total power consumption during the peak hours through the real-time pricing mechanism. However, it also brings privacy concerns. Since the metering data is collected by the ES nearly in real-time, the customer's behavior and habits can be disclosed via the statistical patterns. The privacy problem will hamper the development and deployment of future smart grid. In this paper, we propose a novel metering data transmission scheme in wireless networks. In each Smart Meter (SM), the metering data is split into two parts randomly: Part 1 and Part 2, where the sum of two parts is equal to the original metering data. Part 1 is sent to the ES via cellular link, and Part 2 is sent to the next SM via Wireless Sensor Network (WSN) link. Then the next SM merges Part 2 of the previous SM into its original metering data, and creates the updated metering data. Using the proposed scheme, the metering data received by the ES is randomized, and the customer's privacy can be effectively protected even if some SM cooperates with the ES.

In a heterogeneous wireless sensor network (WSN), relay nodes (RNs) are adopted to relay data packets from sensor nodes (SNs) to the base station (BS). The deployment of the RNs can have a significant impact on connectivity and lifetime of a WSN system. This paper studies the effects of random deployment strategies. We first discuss the biased energy consumption rate problem associated with uniform random deployment. This problem leads to insufficient energy utilization and shortened network lifetime. To overcome this problem, we propose two new random deployment strategies, namely, the lifetime-oriented deployment and hybrid deployment. The former solely aims at balancing the energy consumption rates of RNs across the network, thus extending the system lifetime. However, this deployment scheme may not provide sufficient connectivity to SNs when the given number of RNs is relatively small. The latter reconciles the concerns of connectivity and lifetime extension. Both single-hop and multihop communication models are considered in this paper. With a combination of theoretical analysis and simulated evaluation, this study explores the trade-off between connectivity and lifetime extension in the problem of RN deployment. It also provides a guideline for efficient deployment of RNs in a large-scale heterogeneous WSN.

In this paper we study efficient triangular grid-based sensor deployment planning for coverage when sensor placements are perturbed by random errors around their corresponding grid vertices, where the random errors are modeled by uniform displacements inside error disks of a given finite radius. The average coverage percentage of the sensing field is derived as a function of the length of the grid tiles d, and the radius of the random error disks, R. Our expressions for the average coverage percentage are computed numerically and verified by Monte-Carlo simulations. The analytical methods can be used with other types of grid-based deployment with little modification, such as square grid-based deployment. One appealing feature of grid-based deployment that we observe is that the sensing coverage is rather resilient to random errors. Based on this observation and the quantitative results from our analysis, we discuss several approaches to efficient grid-based deployment planning for coverage and illustrate these through numerical examples.

In this paper we study efficient grid-based sensor deployment planning for coverage when sensor placements are perturbed by random errors around their corresponding grid vertices. We propose a generic approach to evaluate the average coverage percentage of the sensing field. Our generic approach can be used with all types of grid-shapes, all kinds of random error distributions, and different sensing models. We apply the generic approach to two practical deployment scenarios, namely, the triangular grid-based deployment with bounded uniform random errors and unbounded normal random errors. For both cases, the average coverage percentage is computed numerically and verified by Monte-Carlo simulations. Based on the numerical results, we discuss several approaches to efficient grid-based deployment planning for coverage and illustrate these through numerical examples.

Device placement is a fundamental factor in determining the coverage, connectivity, cost and lifetime of a wireless sensor network (WSN). In this paper, we explore the problem of relay node placement in heterogeneous WSN. We formulate a generalized node placement optimization problem aimed at minimizing the network cost with constraints on lifetime and connectivity. Depending on the constraints, two representative scenarios of this problem are described. We characterize the first problem, where relay nodes are not energy constrained, as a minimum set covering problem. We further consider a more challenging scenario, where all nodes are energy limited. As an optimal solution to this problem is difficult to obtain, a two-phase approach is proposed, in which locally optimal design decisions are taken. The placement of the first phase relay nodes (FPRN), which are directly connected to sensor nodes (SN), is modeled as a minimum set covering problem. To ensure the relaying of the traffic from the FPRN to the base station, three heuristic schemes are proposed to place the second phase relay nodes (SPRN). Furthermore, a lower bound on the minimum number of SPRN required for connectivity is provided. The efficiency of our proposals is investigated by numerical examples.

Due to cost constraints and limited sensing capability of sensor nodes, it may be impossible or unrealistic for a wireless sensor network to provide full area coverage in some circumstances. In this paper, we introduce the novel concept of information-oriented sensing coverage, which measures the actual information utility obtained by a wireless sensor network. An information-oriented sensor deployment algorithm is proposed. The algorithm makes use of the fact that in many applications the importance or the information utility at different locations is non-uniform across the sensing field. The effectiveness of the proposed algorithm is studied using simulation. Being a pioneering effort in this area, our work paves a new direction of sensor deployment.

Due to the cost constraint, it is impossible or unnecessary for a wireless sensor network to provide full area coverage in many applications. To mitigate the negative impacts of incomplete coverage, it is critical to realize that the importance of different locations of a sensing field is often non-uniform in practice. By strategically allocating more sensor nodes to the area of more importance, the differential random deployment can effectively decrease the loss for not detecting interesting events, as compared to the uniform random deployment. In this paper, we study the differential random deployment of sensor nodes in depth. We discuss a number of factors that affect the design of the differential random deployment density function. Because of the inherent complexity, we propose a heuristic structure of the differential deployment density function. The effectiveness of our proposal is verified in the performance study via simulation.

Grid-based sensor deployment is an effective and efficient practice for provisioning wireless sensor networks. Previous work has addressed grid-based deployment of sensors in order to guarantee sensing coverage under the assumption that each device can be placed exactly at the grid vertices. However, in reality, the accuracy of device placement may be subject to various errors, which are shown to impair the sensing coverage. To overcome the negative impacts of these errors, the grid resolution and the number of devices to be deployed should be re-evaluated. In this paper, two deployment errors are identified, namely, misalignment and random errors. We derive the minimum number of sensors required by a robust grid-based sensor deployment assuming that the errors are bounded. This research shows that when designing a realistic large-scale grid-based sensor deployment, errors in device placement must be taken into account.

In this paper, the problem of relay node placement in heterogeneous wireless sensor networks (WSNs) is addressed. As all nodes are energy limited, placement strategies aimed at minimizing network cost with constraints on lifetime and connectivity are presented. Based on a two-phase placement framework, a heuristic algorithm to place the second phase relay nodes (SPRNs) is proposed. Furthermore, a lower bound of the optimal solution is derived. The efficacy of our proposal is investigated in both grid networks and random networks. The work of this paper provides guidance for implementing feasible WSNs for civilian applications.

Device deployment is a major challenge to successful implementation of large scale wireless sensor network (WSN) applications. The number and positions of devices determine many intrinsic properties of a WSN system. However, the deployment issue has mostly been confined to studies concerning coverage and connectivity. The impact of device deployment on the usability or system lifetime has been largely neglected. In this research, we investigate how a deployment strategy can affect both connectivity and network lifetime of a WSN. Based on quantitative analysis of such effects, we propose three deployment strategies, namely, connectivity-oriented, lifetime-oriented, and hybrid deployment. The performance of the three strategies is evaluated through simulation. The results of this research provide tangible guidance of efficient provisioning for large scale WSNs.

Node placement strategy is an intrinsic issue when provisioning of a wireless sensor network (WSN). In this paper, we address the placement problem for a class of heterogeneous WSNs, wherein nodes have different energy supplies and functionalities. We formulate a generalized node placement optimization problem aiming at minimizing the network cost with constraints on lifetime and connectivity. We propose a placement scheme with two phases. We model the placement of the first phase relaying nodes (FPRNs) as a minimum set cover problem and a dynamic programming algorithm is developed to solve it. For the placement of the second phase relaying nodes (SPRNs), we derive two fundamental design principles-the far-near strategy and max-min strategy. The implementation of the placement schemes is illustrated by examples. Our proposed mechanism is a first attempt towards facilitating realistic relay node placement in WSNs.

In this paper, we propose a novel single hop-clustering scheme called step wise adaptive clustering hierarchy (SWATCH). SWATCH relieves the CH number variability problem by employing stepwise CH selection in two stages. It is a dynamic and straightforward scheme as LEACH. However, instead of selecting all CHs in one step, SWATCH splits the selection phase into an initial selection stage and an add-on selection stage. The initial selection is similar to LEACH. However, if the number of CHs in the initial selection is below a pre-determined target, the add-on selection will be invoked and will continue until an acceptable number of CHs have been selected. As a result, the number of CHs selected in each round tends to congregate in a narrow range around the optimal value. In order to evaluate the performance of SWATCH, we develop a hierarchical Markov chain model to track the behavior of the system. Numerical results verify our design objective in that the number of selected CHs highly conforms to the optimal value. Based on the results, an optimal number of CHs are greatly reduced the communication energy.

In this paper, we explore the relay node (RN) placement problem in a heterogeneous wireless sensor network (WSN). The objective of the RN placement is to use a minimum number of additional RNs to enable the relaying of given traffic on existing nodes to the base station (BS) under the energy constraints. We assume RNs can adjust their transmission power according to the distance to the intended destination. To make best use of power adaptivity of RN, we propose two heuristic solutions, namely, independent placement with direct allocation (IPDA) and collaborative placement with locally optimal allocation decision (CPLOAD). Furthermore, a lower bound on the minimum number of additional RNs is provided. The effectiveness of our proposals is investigated through simulation using numerical examples.

While a lot of existing research attempts to extend the lifetime of a wireless sensor network (WSN) by designing energy efficient networking protocols, the impact of random device deployment on system lifetime is not stressed enough. Some research efforts have tried to optimize device deployment with respect to lifetime by assuming devices can be placed deliberately. However, the methodologies and solutions therein are not applicable to a randomly deployed large scale WSN. In this research, we propose three random deployment strategies for relay nodes in a heterogeneous WSN, namely, connectivity- oriented, lifetime-oriented and hybrid deployment. We investigate how a strategy can affect both connectivity and network lifetime of a multi-hop heterogeneous WSN, in which relay nodes transmit data to the base station via multi-hop relay. The performance of the three strategies is evaluated through simulations. The results of this research provide a viable solution to the problem of optimizing provisioning of a large scale heterogeneous WSN.

Device deployment is a fundamental issue of WSN provisioning. The types, number, and locations of devices determine many intrinsic properties of a WSN, such as coverage, connectivity, cost and lifetime. In this paper, we explore the problem of the optimal WSN deployment, with an objective of minimizing the network cost with lifetime constraint. We discuss and identify the characteristics of a type of WSN applications. The ultimate objectives of the device deployment for such applications are presented and discussed. We refine a deployment problem in a practical and fundamental scenario. We model this problem with the minimum set covering problem. Based on a recursive algorithm, a deterministic deployment strategy is proposed.

This paper proposes a multidimensional Markov model to analyze performance of the IEEE802.11e EDCF protocol. Based on this model, we present extensive performance evaluation in terms of saturation throughput (ST), throughput ratios, and access delay of flows of distinct priorities under RTS/CTS mode. We also provide quantitative analysis of the impact of prioritized parameters, i.e., arbitration interframe space (AIFS), contention window (CW) on QoS differentiation. The accuracy of the proposed model is verified by means of comparing the numerical results obtained from both analytical model and simulations.