Limited energy in each node is the major design constraint in wireless sensor networks (WSNs). To overcome this limit, wireless rechargeable sensor networks (WRSNs) have been proposed and studied extensively over the last few years. In a typical WRSN, batteries in sensor nodes can be replenished by a mobile charger that periodically travels along a certain trajectory in the sensing area. To maximize the charged energy in sensor nodes, one fundamental question is how to control the traveling velocity of the charger. In this paper, we first identify the optimal velocity control as a key design objective of mobile wireless charging in WRSNs. We then formulate the optimal charger velocity control problem on arbitrarily-shaped irregular trajectories in a 2D space. The problem is proved to be NP-hard, and hence a heuristic solution with a provable upper bound is developed using novel spatial and temporal discretization. We also derive the optimal velocity control for moving the charger along a linear (1D) trajectory commonly seen in many WSN applications. Extensive simulations show that the network lifetime can be extended by $2.5 \times$ with the proposed velocity control mechanisms.

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Near-Optimal Velocity Control for Mobile Charging in Wireless Rechargeable Sensor Networks

Wireless sensor networks (WSNs) are a collection of several small and inexpensive battery-powered nodes, commonly used to monitor regions of interests and to collect data from the environment. Several issues exist in routing data packets through WSN, but the most crucial problem is energy. There are a number of routing approaches in WSNs that address the issue of energy by the use of different energy-efficient methods. This paper, presents a brief summary of routing and related issues in WSNs. The most recent energy-efficient data routing approaches are reviewed and categorized based on their aims and methodologies. The traditional battery based energy sources for sensor nodes and the conventional energy harvesting mechanisms that are widely used to in energy replenishment in WSN are reviewed. Then a new emerging energy harvesting technology that uses piezoelectric nanogenerators to supply power to nanosensor; the type of sensors that cannot be charged by conventional energy harvesters are explained. The energy consumption reduction routing strategies in WSN are also discussed. Furthermore, comparisons of the variety of energy harvesting mechanisms and battery power routing protocols that have been discussed are presented, eliciting their advantages, disadvantages and their specific feature. Finally, a highlight of the challenges and future works in this research domain is presented.

Energy harvesting and battery power based routing in wireless sensor networks

Wireless sensor networks (WSNs) consist of large number of small sized sensor nodes, whose main task is to sense the desired phenomena in a particular region of interest. These networks have large number of applications such as habitat monitoring, disaster management, security and military etc. Sensor nodes are very small in size and have limited processing capability as these nodes have very low battery power. WSNs are also prone to failure, due to low battery power constraint. Data aggregation is an energy efficient technique in WSNs. Due to high node density in sensor networks same data is sensed by many nodes, which results in redundancy. This redundancy can be eliminated by using data aggregation approach while routing packets from source nodes to base station. Researchers still face trouble to select an efficient and appropriate data aggregation technique from the existing literature of WSNs. This research work depicts a broad methodical literature analysis of data aggregation in the area of WSNs in specific. In this survey, standard methodical literature analysis technique is used based on a complete collection of 123 research papers out of large collection of 932 research papers published in 20 foremost workshops, symposiums, conferences and 17 prominent journals. The current status of data aggregation in WSNs is distributed into various categories. Methodical analysis of data aggregation in WSNs is presented which includes techniques, tools, methodology and challenges in data aggregation. The literature covered fifteen types of data aggregation techniques in WSNs. Detailed analysis of this research work will help researchers to find the important characteristics of data aggregation techniques and will also help to select the most suitable technique for data aggregation. Research issues and future research directions have also been suggested in this research literature.

Data Aggregation in Wireless Sensor Networks: Previous Research, Current Status and Future Directions

Energy constraint is a critical issue in the development of wireless sensor networks (WSNs) because sensor nodes are generally powered by batteries. Recently, wireless rechargeable sensor networks (WRSNs), which introduce wireless mobile chargers (MCs) to replenish energy for nodes, have been proposed to resolve the root cause of energy limitations in WSNs. However, existing wireless charging algorithms cannot fully leverage the mobility of MCs because unity between the energy replenishment process and mobile data collection has yet to be realized. Thus, in this paper, a joint energy replenishment and data collection algorithm for WRSNs is proposed. In this algorithm, the network is divided into multiple clusters based on a ${K}$ -means algorithm. Two MCs visit the anchor point in each cluster by moving along the shortest Hamiltonian cycle in opposite directions. The positions of anchor points are calculated by the base station (BS) based on the energy distribution in each cluster. A spare MC is assigned to the network in case either of the two MCs depletes its energy before reaching the BS. After the two MCs’ current tours are over, a semi-Markov model is proposed for energy prediction so anchor points can be updated in the next round. Simulation results demonstrate the semi-Markov-based energy prediction model is highly precise, and the proposed algorithm can replenish energy for network energy effectively.

A Joint Energy Replenishment and Data Collection Algorithm in Wireless Rechargeable Sensor Networks

Data aggregation is a very important method to conserve energy by eliminating the inherent redundancy of raw data in wireless sensor networks (WSNs). In this article, we developed an automatic auto regressive-integrated moving averagemodeling-based data aggregation scheme in WSNs. The main idea behind this scheme is to decrease the number of transmitted data values between sensor nodes and aggregators by utilizing time series prediction model. The proposed scheme can effectively save the precious battery energy of wireless sensor nodes while keeping the predicted data values of aggregators within application-defined error threshold. We show through experiments with real data that the predicted data values of our proposed scheme fit the real sensed data values very well and fewer messages are transmitted between sensor nodes and aggregators than the native data aggregation scheme. Furthermore, the characteristics of the proposed data aggregation scheme are also discussed in this article.

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Automatic ARIMA modeling-based data aggregation scheme in wireless sensor networks

http://sina.sharif.ac.ir/~movaghar/Yousefi-COMCOM12.pdf

Structure-free real-time data aggregation in wireless sensor networks

Data aggregation is an effective technique which is introduced to save energy by reducing packet transmissions in WSNs. However, it extends the delay at the intermediate nodes, so it can complicate the handling of delay-constrained data in event-critical applications. Besides, the structure-based aggregation as the dominant data gathering approach in WSNs suffers from high maintenance overhead in dynamic scenarios for event-based applications. In this paper, to make aggregation more efficient, we design a novel structure-free Real-time Data Aggregation protocol, RDAG, using a Real-time Data-aware Routing policy and a Judiciously Waiting policy for spatial and temporal convergence of packets. Extensive simulations in NS-2 verify the superiority of RDAG in WSNs.

/pdf/rdag-a-structure-free-real-time-data-aggregation-protocol-5exntv1dpj.pdf

RDAG: A structure-free real-time data aggregation protocol for wireless sensor networks

Lifetime is the most important concern in Wireless Sensor Networks (WSNs) due to limited battery power of sensor nodes. Moreover, a WSN should be capable of timely fulfilling its mission without losing important information in event-critical applications. In this paper, we focus on designing an energy-efficient and energy-aware real-time routing algorithm aiming to explore the long lifetime routing schemes in which delay constraint is satisfied in the presence of lossy communication links. To achieve this goal, our energy-aware forwarding protocol utilizes an optimum distance real-time routing algorithm to minimize energy consumption in unreliable WSNs. Simulation results reveal that the proposed algorithm outperforms other existing schemes in terms of energy consumption, network lifetime, and miss ratio.

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Naser Alinaghipour

Papers

Structure-free real-time data aggregation in wireless sensor networks

RDAG: A structure-free real-time data aggregation protocol for wireless sensor networks

Long lifetime real-time routing in unreliable Wireless Sensor Networks