Biblio

Wireless Sensor Networks (WSNs) are applied in environmental monitoring, military surveillance, etc., whereas these applications focuses on providing security for sensed data and the nodes are available for a long time. Hence, we propose DEAVD protocol for secure data exchange with limited usage of energy. The DEAVD protocol compresses data to reduces the energy consumption and implements an energy efficient encryption and decryption technique using voronoi diagram paradigm. Thus, there is an improvement in the proposed protocol with respect to security due to the concept adapted during data encryption and aggregation.

Security awareness and energy efficiency are two crucial optimization issues present in MANET where the network topology gets adequately changed and is not predictable which affects the lifetime of the MANET. They are extensively analyzed to improvise the lifetime of the MANET. This paper concentrates on the design of an energy-efficient security-aware fuzzy-based clustering (SFLC) technique to make the network secure and energy-efficient. The selection of cluster heads (CHD) process using fuzzy logic (FL) involves the trust factor as an important input variable. Once the CHDs are elected successfully, clusters will be constructed and start to communication with one another as well as the base station (BS). The presented SFLC model is simulated using NS2 and the performance is validated in terms of energy, lifetime and computation time.

Low-power wireless networks provide IPv6 connectivity through 6LoWPAN, a set of standards to aggressively compress IPv6 packets over small maximum transfer unit (MTU) links such as 802.15.4.The entire purpose of IP was to interconnect different networks, but we find that different 6LoWPAN implementations fail to reliably communicate with one another. These failures are due to stacks implementing different subsets of the standard out of concern for code size. We argue that this failure stems from 6LoWPAN's design, not implementation, and is due to applying traditional Internet protocol design principles to low- power networks.We propose three design principles for Internet protocols on low-power networks, designed to prevent similar failures in the future. These principles are based around the importance of providing flexible tradeoffs between code size and energy efficiency. We apply these principles to 6LoWPAN and show that the modified protocol provides a wide range of implementation strategies while allowing implementations with different strategies to reliably communicate.

The use of Electric Vehicle (EV) is growing rapidly due to its environmental benefits. However, the major problem of these vehicles is their limited battery, the lack of charging stations and the re-charge time. Introducing Information and Communication Technologies, in the field of EV, will improve energy efficiency, energy consumption predictions, availability of charging stations, etc. The Internet of Vehicles based only on Electric Vehicles (IoEV) is a complex system. It is composed of vehicles, humans, sensors, road infrastructure and charging stations. All these entities communicate using several communication technologies (ZigBee, 802.11p, cellular networks, etc). IoEV is therefore vulnerable to significant attacks such as DoS, false data injection, modification. Hence, security is a crucial factor for the development and the wide deployment of Internet of Electric Vehicles (IoEV). In this paper, we present an overview of security issues of the IoEV architecture and we highlight open issues that make the IoEV security a challenging research area in the future.

Underwater sensor networks (UWSN) are a division of classical wireless sensor networks (WSN), which are designed to accomplish both military and civil operations, such as invasion detection and underwater life monitoring. Underwater sensor nodes operate using the energy provided by integrated limited batteries, and it is a serious challenge to replace the battery under the water especially in harsh conditions with a high number of sensor nodes. Here, energy efficiency confronts as a very important issue. Besides energy efficiency, data privacy is another essential topic since UWSN typically generate delicate sensing data. UWSN can be vulnerable to silent positioning and listening, which is injecting similar adversary nodes into close locations to the network to sniff transmitted data. In this paper, we discuss the usage of compressive sensing (CS) and energy harvesting (EH) to improve the lifetime of the network whilst we suggest a novel encryption decision method to maintain privacy of UWSN. We also deploy a Mixed Integer Programming (MIP) model to optimize the encryption decision cases which leads to an improved network lifetime.

In recent trends, Cyber-Physical Systems (CPS) and Internet of Things interpret an evolution of computerized integration connectivity. The specific research challenges in CPS as security, privacy, data analytics, participate sensing, smart decision making. In addition, The challenges in Wireless Sensor Network (WSN) includes secure architecture, energy efficient protocols and quality of services. In this paper, we present an architectures of CPS and its protocols and applications. We propose software related mobile sensing paradigm namely Mobile Sensor Information Agent (MSIA). It works as plug-in based for CPS middleware and scalable applications in mobile devices. The working principle MSIA is acts intermediary device and gathers data from a various external sensors and its upload to cloud on demand. CPS needs tight integration between cyber world and man-made physical world to achieve stability, security, reliability, robustness, and efficiency in the system. Emerging software-defined networking (SDN) can be integrated as the communication infrastructure with CPS infrastructure to accomplish such system. Thus we propose a possible SDN-based CPS framework to improve the performance of the system.

Modern Internet of Things networks are often proprietary, although based on open standards, or are built on the basis of conventional Wi-Fi network, which does not allow the use of energy-saving modes and limits the range of solutions used. The paper is devoted to the study and comparison of two solutions based on Wi-Fi and Bluetooth with the functions of a self-organizing network and switching between transmission channels. The power consumption in relation to specific actions and volumes of transmitted data is investigated; a conclusion is drawn on the conditions for the application of a particular technology.

Sybil attacks, wherein a network is subverted by forging node identities, remains an open issue in wireless sensor networks (WSNs). This paper proposes a scheme, called Location and Communication ID (LCID) based detection, which employs residual energy, communication ID and location information of sensor nodes for Sybil attacks prevention. Moreover, LCID takes into account the resource constrained nature of WSNs and enhances energy conservation through hierarchical routing. Sybil nodes are purged before clusters formation to ensure that only legitimate nodes participate in clustering and data communication. CH selection is based on the average energy of the entire network to load-balance energy consumption. LCID selects a CH if its residual energy is greater than the average network energy. Furthermore, the workload of CHs is equally distributed among sensor nodes. A CH once selected cannot be selected again for 1/p rounds, where p is the CH selection probability. Simulation results demonstrate that, as compared to an eminent scheme, LCID has a higher Sybil attacks detection ratio, higher network lifetime, higher packet reception rate at the BS, lower energy consumption, and lower packet loss ratio.

As the ocean covers 70% of the Earth's surface, it becomes inevitable to develop or extend underwater applications. Compared to Visible Light medium, Acoustic medium has been widely used to transmit the data from source to destination in underwater communication. Data transmission, however, has the limitation such as propagation delay, reliability, power constraints, etc. Although underwater MAC protocols have been developed to overcome these challenges, there are still some drawbacks due to the harsh underwater environment. Therefore, the selection mechanism for underwater multi-media communication is proposed inside Medium Access Control (MAC) layer. In this paper, the main focus is to select the appropriate medium based on the distance between nodes and transmission power. The result of performance evaluation shows that this multimedia approach can complement the existing underwater single medium communication. As a result, underwater multimedia mechanism increases the reliability and energy efficiency in data transmission.

There is an ongoing search for finding energy-efficient solutions in multi-core computing platforms. Approximate computing is one such solution leveraging the forgiving nature of applications to improve the energy efficiency at different layers of the computing platform ranging from applications to hardware. We are interested in understanding the benefits of approximate computing in the realm of Apache Hadoop and its applications. A few mechanisms for introducing approximation in programming models include sampling input data, skipping selective computations, relaxing synchronization, and user-defined quality-levels. We believe that it is straightforward to apply the aforementioned mechanisms to conserve energy in Hadoop clusters as well. The emerging trend of approximate computing motivates us to systematically investigate thermal profiling of approximate computing strategies in this research. In particular, we design a thermal-aware approximate computing framework called tHadoop2, which is an extension of tHadoop proposed by Chavan et al. We investigated the thermal behavior of a MapReduce application called Pi running on Hadoop clusters by varying two input parameters - number of maps and number of sampling points per map. Our profiling results show that Pi exhibits inherent resilience in terms of the number of precision digits present in its value.

Bluetooth Low Energy is a fast growing protocol which has gained wide acceptance during last years. Key features for this growth are its high data rate and its ultra low energy consumption, making it the perfect candidate for piconets. However, the lack of expandability without serious impact on its energy consumption profile, prevents its adoption on more complex systems which depend on long network lifetime. Thus, a lot of academic research has been focused on the solution of BLE expandability problem and BLE mesh has been introduced on the latest Bluetooth version. In our point of view, most of the related work cannot be efficiently implemented in networks which are mostly comprised of constrained-resource nodes. Thus, we propose a new energy efficient tree algorithm for BLE static constrained-resources networks, which achieves a longer network lifetime by both reducing as much as possible the number of needed connection events and balancing the energy dissipation in the network.

In sensor-enabled Internet of Things (IoT), nodes are deployed in an open and remote environment, therefore, are vulnerable to a variety of attacks. Recently, trust-based schemes have played a pivotal role in addressing nodes' misbehavior attacks in IoT. However, the existing trust-based schemes apply network wide dissemination of the control packets that consume excessive energy in the quest of trust evaluation, which ultimately weakens the network lifetime. In this context, this paper presents an energy efficient trust evaluation (EETE) scheme that makes use of hierarchical trust evaluation model to alleviate the malicious effects of illegitimate sensor nodes and restricts network wide dissemination of trust requests to reduce the energy consumption in clustered-sensor enabled IoT. The proposed EETE scheme incorporates three dilemma game models to reduce additional needless transmissions while balancing the trust throughout the network. Specially: 1) a cluster formation game that promotes the nodes to be cluster head (CH) or cluster member to avoid the extraneous cluster; 2) an optimal cluster formation dilemma game to affirm the minimum number of trust recommendations for maintaining the balance of the trust in a cluster; and 3) an activity-based trust dilemma game to compute the Nash equilibrium that represents the best strategy for a CH to launch its anomaly detection technique which helps in mitigation of malicious activity. Simulation results show that the proposed EETE scheme outperforms the current trust evaluation schemes in terms of detection rate, energy efficiency and trust evaluation time for clustered-sensor enabled IoT.

Link quality, trust management and energy efficiency are considered as main factors that affect the performance and lifetime of Mobile CrowdSensing (MCS). Routing packets toward the sink node can be a daunting task if aforementioned factors are considered. Correspondingly, routing packets by considering only shortest path or residual energy lead to suboptimal data forwarding. To this end, we propose a Fuzzy logic based Routing (FR) solution that incorporates social behaviour of human beings, link quality, and node quality to make the optimal routing decision. FR leverages friendship mechanism for trust management, Signal to Noise Ratio (SNR) to assure good link quality node selection, and residual energy for long lasting sensor lifetime. Extensive simulations show that the FR solution outperforms the existing approaches in terms of network lifetime and packet delivery ratio.

Sybil security threat in vehicular ad hoc networks (VANETs) has attracted much attention in recent times. The attacker introduces malicious nodes with multiple identities. As the roadside unit fails to synchronize its clock with legitimate vehicles, unintended vehicles are identified, and therefore erroneous messages will be sent to them. This paper proposes a novel biologically inspired spider-monkey time synchronization technique for large-scale VANETs to boost packet delivery time synchronization at minimized energy consumption. The proposed technique is based on the metaheuristic stimulated framework approach by the natural spider-monkey behavior. An artificial spider-monkey technique is used to examine the Sybil attacking strategies on VANETs to predict the number of vehicular collisions in a densely deployed challenge zone. Furthermore, this paper proposes the pseudocode algorithm randomly distributed for energy-efficient time synchronization in two-way packet delivery scenarios to evaluate the clock offset and the propagation delay in transmitting the packet beacon message to destination vehicles correctly. The performances of the proposed technique are compared with existing protocols. It performs better over long transmission distances for the detection of Sybil in dynamic VANETs' system in terms of measurement precision, intrusion detection rate, and energy efficiency.

With the rapid and radical evolution of information and communication technology, energy consumption for wireless communication is growing at a staggering rate, especially for wireless multimedia communication. Recently, reducing energy consumption in wireless multimedia communication has attracted increasing attention. In this paper, we propose an energy-efficient wireless image transmission scheme based on adaptive block compressive sensing (ABCS) and SoftCast, which is called ABCS-SoftCast. In ABCS-SoftCast, the compression distortion and transmission distortion are considered in a joint manner, and the energy-distortion model is formulated for each image block. Then, the sampling rate (SR) and power allocation factors of each image block are optimized simultaneously. Comparing with conventional SoftCast scheme, experimental results demonstrate that the energy consumption can be greatly reduced even when the receiving image qualities are approximately the same.

This work targets the automated minimum-energy optimization of Quantized Neural Networks (QNNs) - networks using low precision weights and activations. These networks are trained from scratch at an arbitrary fixed point precision. At iso-accuracy, QNNs using fewer bits require deeper and wider network architectures than networks using higher precision operators, while they require less complex arithmetic and less bits per weights. This fundamental trade-off is analyzed and quantified to find the minimum energy QNN for any benchmark and hence optimize energy-efficiency. To this end, the energy consumption of inference is modeled for a generic hardware platform. This allows drawing several conclusions across different benchmarks. First, energy consumption varies orders of magnitude at iso-accuracy depending on the number of bits used in the QNN. Second, in a typical system, BinaryNets or int4 implementations lead to the minimum energy solution, outperforming int8 networks up to 2-10× at iso-accuracy. All code used for QNN training is available from https://github.com/BertMoons/.

State-of-the-art convolutional neural networks (ConvNets) are now able to achieve near human performance on a wide range of classification tasks. Unfortunately, current hardware implementations of ConvNets are memory power intensive, prohibiting deployment in low-power embedded systems and IoE platforms. One method of reducing memory power is to exploit the error resilience of ConvNets and accept bit errors under reduced supply voltages. In this paper, we extensively study the effectiveness of this idea and show that further savings are possible by injecting bit errors during ConvNet training. Measurements on an 8KB SRAM in 28nm UTBB FD-SOI CMOS demonstrate supply voltage reduction of 310mV, which results in up to 5.4× leakage power reduction and up to 2.9× memory access power reduction at 99% of floating-point classification accuracy, with no additional hardware cost. To our knowledge, this is the first silicon-validated study on the effect of bit errors in ConvNets.

Resilience in High Performance Computing (HPC) is a constraining factor for bringing applications to the upcoming exascale systems. Resilience techniques must be able to scale to handle the increasing number of expected errors in an energy efficient manner. Since the purpose of running applications on HPC systems is to perform large scale computations as quick as possible, resilience methods should not add a large delay to the time to completion of the application. In this paper we introduce a novel technique to detect and recover from transient errors in HPC applications. One of the features of our technique is that the energy budget allocated to resilience can be adjusted depending on the operator's resilience needs. For example, on synthetic data, the technique can detect about 50% of transient errors while only using 20% of the dynamic energy required for running the application. For a 60% energy budget, an application that uses 10k cores and takes 128 hours to run, will only require 10% longer to complete.

Near-sensor data analytics is a promising direction for internet-of-things endpoints, as it minimizes energy spent on communication and reduces network load - but it also poses security concerns, as valuable data are stored or sent over the network at various stages of the analytics pipeline. Using encryption to protect sensitive data at the boundary of the on-chip analytics engine is a way to address data security issues. To cope with the combined workload of analytics and encryption in a tight power envelope, we propose Fulmine, a system-on-chip (SoC) based on a tightly-coupled multi-core cluster augmented with specialized blocks for compute-intensive data processing and encryption functions, supporting software programmability for regular computing tasks. The Fulmine SoC, fabricated in 65-nm technology, consumes less than 20mW on average at 0.8V achieving an efficiency of up to 70pJ/B in encryption, 50pJ/px in convolution, or up to 25MIPS/mW in software. As a strong argument for real-life flexible application of our platform, we show experimental results for three secure analytics use cases: secure autonomous aerial surveillance with a state-of-the-art deep convolutional neural network (CNN) consuming 3.16pJ per equivalent reduced instruction set computer operation, local CNN-based face detection with secured remote recognition in 5.74pJ/op, and seizure detection with encrypted data collection from electroencephalogram within 12.7pJ/op.

Wireless sensor networks have been widely utilized in many applications such as environment monitoring and controlling. Appropriate sensor deployment scheme to achieve the maximal coverage is crucial for effectiveness of sensor network. In this paper, we study coverage optimization problem with hopping sensors. Although similar problem has been investigated when each mobile sensor has continuous dynamics, the problem is different for hopping sensor which has discrete and constraint dynamics. Based on the characteristics of hopping, we obtain dynamics equation of hopping sensors. Then we propose an enhanced virtual force algorithm as a deployment scheme to improve the coverage. A combination of attractive and repulsive forces generated by Voronoi neighbor sensors, obstacles and the centroid of local Voronoi cell is used to determine the motion paths for hopping sensors. Furthermore, a timer is designed to adjust the movement sequence of sensors, such that unnecessary movements can be reduced. Simulation results show that optimal coverage can be accomplished by hopping sensors in an energy efficient manner.

With the rapid development of Wireless Sensor Networks (WSNs), besides the energy efficient, Quality of Service (QoS) supported and the validity of packet transmission should be considered under some circumstances. In this paper, according to summing up LEACH protocol's advantages and defects, combining with trust evaluation mechanism, energy and QoS control, a trust-based QoS routing algorithm is put forward. Firstly, energy control and coverage scale are adopted to keep load balance in the phase of cluster head selection. Secondly, trust evaluation mechanism is designed to increase the credibility of the network in the stage of node clusting. Finally, in the period of information transmission, verification and ACK mechanism also put to guarantee validity of data transmission. In this paper, it proposes the improved protocol. The improved protocol can not only prolong nodes' life expectancy, but also increase the credibility of information transmission and reduce the packet loss. Compared to typical routing algorithms in sensor networks, this new algorithm has better performance.

A key challenge of future mobile communication research is to strike an attractive compromise between wireless network's area spectral efficiency and energy efficiency. This necessitates a clean-slate approach to wireless system design, embracing the rich body of existing knowledge, especially on multiple-input-multiple-ouput (MIMO) technologies. This motivates the proposal of an emerging wireless communications concept conceived for single-radio-frequency (RF) large-scale MIMO communications, which is termed as SM. The concept of SM has established itself as a beneficial transmission paradigm, subsuming numerous members of the MIMO system family. The research of SM has reached sufficient maturity to motivate its comparison to state-of-the-art MIMO communications, as well as to inspire its application to other emerging wireless systems such as relay-aided, cooperative, small-cell, optical wireless, and power-efficient communications. Furthermore, it has received sufficient research attention to be implemented in testbeds, and it holds the promise of stimulating further vigorous interdisciplinary research in the years to come. This tutorial paper is intended to offer a comprehensive state-of-the-art survey on SM-MIMO research, to provide a critical appraisal of its potential advantages, and to promote the discussion of its beneficial application areas and their research challenges leading to the analysis of the technological issues associated with the implementation of SM-MIMO. The paper is concluded with the description of the world's first experimental activities in this vibrant research field.
 

Wireless Sensor Networks (WSNs) are deployed to monitor the assets (endangered species) and report the locations of these assets to the Base Station (BS) also known as Sink. The hunter (adversary) attacks the network at one or two hops away from the Sink, eavesdrops the wireless communication links and traces back to the location of the asset to capture them. The existing solutions proposed to preserve the privacy of the assets lack in energy efficiency as they rely on random walk routing technique and fake packet injection technique so as to obfuscate the hunter from locating the assets. In this paper we present an energy efficient privacy preserved routing algorithm where the event (i.e., asset) detected nodes called as source nodes report the events' location information to the Base Station using phantom source (also known as phantom node) concept and a-angle anonymity concept. Routing is done using existing greedy routing protocol. Comparison through simulations shows that our solution reduces the energy consumption and delay while maintaining the same level of privacy as that of two existing popular techniques.
 

Wireless Mesh Network (WMN) is a promising wireless network architecture having potential of last few miles connectivity. There has been considerable research work carried out on various issues like design, performance, security etc. in WMN. Due to increasing interest in WMN and use of smart devices with bandwidth hungry applications, WMN must be designed with objective of energy efficient communication. Goal of this paper is to summarize importance of energy efficiency in WMN. Various techniques to bring energy efficient solutions have also been reviewed.