Yi Zhang

Bio: Yi Zhang is an academic researcher from University of Science and Technology of China. The author has contributed to research in topics: Network packet & Wireless sensor network. The author has an hindex of 1, co-authored 2 publications receiving 13 citations.

A Novel Energy Efficient Wireless Sensor MAC Protocol

Abstract: Wireless sensor networks (WSN) have been widely used in many important areas. Medium access control (MAC) protocols have a significant influence on the function and performance of WSN. In existing protocols such as sensor MAC (SMAC), the sensor nodes reduce energy consumption by introducing an active/sleep duty cycle, which always leads to more control packets. These control packets waste a lot of energy. Other contention-based MAC protocols either cannot solve the idle listening or fail to consider the complexity of the protocol. Based on SMAC, we propose a novel contention-based MAC protocol, which decreases the control packets by modifying the control packets message and canceling time synchronization. The simulation result indicates that the performance of our protocol is much better than that of SMAC protocol in energy consumption.

A sequential chain directional transmission-based localization algorithm

Abstract: The accuracy of localization is a significant criterion to evaluate the practical utility of localization algorithm in wireless sensor networks. By analyzing the impact of the network topology on the position accuracy of representative localization algorithms, in this paper, we proposed a novel Sequential Chain Directional Transmission-based Localization Algorithm (SCDTLA), which uses the topology control scheme to construct a sequential chain for every node in wireless sensor networks. This algorithm can make the data packets to be transmitted directionally by sensors while using the omni-directional antenna under flooding protocol. In such scenes where sensor nodes are uniformly distributed, the simulation results show that our localization algorithm can position much more accurately than DV hop in regular and especially irregular network topologies with lower communication overhead.

Energy-aware lazy scheduling algorithm for energy-harvesting sensor nodes

Abstract: The main problem in dealing with energy-harvesting (EH) sensor nodes is represented by the scarcity and non-stationarity of powering, due to the nature of the renewable energy sources. In this work, the authors address the problem of task scheduling in processors located in sensor nodes powered by EH sources. Some interesting solutions have appeared in the literature in the recent past, as the lazy scheduling algorithm (LSA), which represents a performing mix of scheduling effectiveness and ease of implementation. With the aim of achieving a more efficient and conservative management of energy resources, a new improved LSA solution is here proposed. Indeed, the automatic ability of foreseeing at run-time the task energy starving (i.e. the impossibility of finalizing a task due to the lack of power) is integrated within the original LSA approach. Moreover, some modifications have been applied in order to reduce the LSA computational complexity and thus maximizing the amount of energy available for task execution. The resulting technique, namely energy-aware LSA, has then been tested in comparison with the original one, and a relevant performance improvement has been registered both in terms of number of executable tasks and in terms of required computational burden.

An Energy-Latency-Efficient MAC Protocol for Wireless Sensor Networks

Abstract: Because nodes are usually battery-powered, the energy presents a very scarce resource in wireless sensor networks. For this reason, the design of medium access control had to take energy efficiency as one of its hottest concerns. Accordingly, in order to improve the energy performance of MAC schemes in wireless sensor networks, several ways can be followed. In fact, some researchers try to limit idle listening while others focus on mitigating overhearing (i.e. a node can hear a packet which is destined to another node) or reducing the number of the used control packets. We, in this paper, propose a new hybrid MAC protocol termed ELE-MAC (i.e. Energy Latency Efficient MAC). The ELE-MAC major design goals are energy and latency efficiencies. It adopts less control packets than SMAC in order to preserve energy. We carried out ns2 simulations to evaluate the performance of the proposed protocol. Thus, our simulation’s results prove the ELE-MAC energy efficiency. Additionally, our solution performs statistically the same or better latency characteristic compared to adaptive SMAC. Keywords—Control packet, energy efficiency, medium access control, wireless sensor networks.

Waterfalls Partial Aggregation in Wireless Sensor Networks

Abstract: In wireless sensor networks (WSNs), energy saving is a critical issue. Many research works have been undertaken to save energy. Data aggregation is one of the schemes that save energy by reducing the amount of data transmission. Normally, researchers focus on saving energy by aggregating multiple data or turning to achieving short transmission delay in data aggregation; few of them are concerned with network lifetime. This work achieves an optimum network lifetime by balancing energy consumption among nodes in network. Here, we propose a waterfalls partial aggregation, controlled by a set of waterfalls pushing rate vectors. The first contribution of this paper is to propose a waterfalls partial aggregation and to model it with queuing theory. The second contribution is that the optimum network lifetime is achieved mathematically and a near optimum algorithm is proposed for a given transmission delay. The results are compared with existing energy efficient algorithms and the evaluation results show the efficiency of proposed algorithm.

Mixed Observability Markov Decision Processes for Overall Network Performance Optimization in Wireless Sensor Networks

Abstract: Optimizing overall performance of Wireless Sensor Networks (WSNs) is important due to the limited resources available to nodes. Several aspects of this optimization problem have been studied (e.g. improving Medium Access Control (MAC) protocols, routing, energy management) mostly separately, although there is a strong inter-connection between them. In this paper an Artificial Intelligence (AI) based framework is presented to address this problem. Mixed-Observability Markov Decision Processes (MOMDPs) are used to effectively model multiple aspects of WSNs in stochastic environments including MAC in data link layer, routing in network layer, data aggregation, power management, etc. MOMDPs distinguish between full and partial observability, hence they are more efficient than other similar AI methods. The proposed framework provides global optimization of user-defined performance metrics, e.g. minimization of time delay, energy consumption and data inaccuracy. Near-optimal joint network policies are obtained via offline approximation of optimal MOMDP solutions and they are distributed among the individual nodes. Resulting node-policies place effectively no additional computational overhead on nodes in runtime. Experiments evaluate the framework by demonstrating near-optimal solutions for a small-scale WSN in detail in case of given tradeoff criteria. The proposed approach produces better joint network behavior in 5 out of 6 cases compared to other two standard methods in simulation by increasing overall network performance by more than 20% in average.

An energy aware approach for task scheduling in energy-harvesting sensor nodes

Abstract: One of the most challenging issues for nowadays Wireless Sensor Networks (WSNs) is represented by the capability of self-powering the network sensor nodes by means of suitable Energy Harvesting (EH) techniques. However, the nature of such energy captured from the environment is often irregular and unpredictable and therefore some intelligence is required to efficiently use it for information processing at the sensor level. In particular in this work the authors address the problem of task scheduling in processors located in WSN nodes powered by EH sources. The authors' objective consists in employing a conservative scheduling paradigm in order to achieve a more efficient management of energy resources. To prove such a claim, the recently advanced Lazy Scheduling Algorithm (LSA) has been taken as reference and integrated with the automatic ability of foreseeing at runtime the task energy starving, i.e. the impossibility of finalizing a task due to the lack of power. The resulting technique, namely Energy Aware Lazy Scheduling Algorithm (EA-LSA), has then been tested in comparison with the original one and a relevant performance improvement has been registered in terms of number of executable tasks.