Biblio

Wireless sensor networks consist of various sensors that are deployed to monitor the physical world. And many existing security schemes use traditional cryptography theory to protect message content and contextual information. However, we are concerned about location security of nodes. In this paper, we propose an anonymous routing strategy for preserving location privacy (ARPLP), which sets a proxy source node to hide the location of real source node. And the real source node randomly selects several neighbors as receivers until the packets are transmitted to the proxy source. And the proxy source is randomly selected so that the adversary finds it difficult to obtain the location information of the real source node. Meanwhile, our scheme sets a branch area around the sink, which can disturb the adversary by increasing the routing branch. According to the analysis and simulation experiments, our scheme can reduce traffic consumption and communication delay, and improve the security of source node and base station.

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.

Data's security being important aspect of the today's internet is gaining more importance day by day. With the increase in online data exchange, transactions and payments; secure payment and secure data transfers have become an area of concern. Cryptography makes the data transmission over the internet secure by various methods, algorithms. Cryptography helps in avoiding the unauthorized people accessing the data by authentication, confidentiality, integrity and non-repudiation. In order to securely transmit the data many cryptographic algorithms are present, but the algorithm to be used should be robust, efficient, cost effective, high performance and easily deployable. Choosing an algorithm which suits the customer's requirement is an utmost important task. The proposed work discusses different symmetric key cryptographic algorithms like DES, 3DES, AES and Blowfish by considering encryption time, decryption time, entropy, memory usage, throughput, avalanche effect and energy consumption by practical implementation using java. Practical implementation of algorithms has been highlighted in proposed work considering tradeoff performance in terms of cost of various parameters rather than mere theoretical concepts. Battery consumption and avalanche effect of algorithms has been discussed. It reveals that AES performs very well in overall performance analysis among considered algorithms.

An advanced metering infrastructure (AMI) allows real-time fine-grained monitoring of the energy consumption data of individual consumers. Collected metering data can be used for a multitude of applications. For example, energy demand forecasting, based on the reported fine-grained consumption, can help manage the near future energy production. However, fine- grained metering data reporting can lead to privacy concerns. It is, therefore, imperative that the utility company receives the fine-grained data needed to perform the intended demand response service, without learning any sensitive information about individual consumers. In this paper, we propose an anonymous privacy preserving fine-grained data aggregation scheme for AMI networks. In this scheme, the utility company receives only the distribution of the energy consumption by the consumers at different time slots. We leverage a network tree topology structure in which each smart meter randomly reports its energy consumption data to its parent smart meter (according to the tree). The parent node updates the consumption distribution and forwards the data to the utility company. Our analysis results show that the proposed scheme can preserve the privacy and security of individual consumers while guaranteeing the demand response service.

The advent of smart grids offers us the opportunity to better manage the electricity grids. One of the most interesting challenges in the modern grids is the consumer demand management. Indeed, the development in Information and Communication Technologies (ICTs) encourages the development of demand-side management systems. In this paper, we propose a distributed energy demand scheduling approach that uses minimal interactions between consumers to optimize the energy demand. We formulate the consumption scheduling as a constrained optimization problem and use game theory to solve this problem. On one hand, the proposed approach aims to reduce the total energy cost of a building's consumers. This imposes the cooperation between all the consumers to achieve the collective goal. On the other hand, the privacy of each user must be protected, which means that our distributed approach must operate with a minimal information exchange. The performance evaluation shows that the proposed approach reduces the total energy cost, each consumer's individual cost, as well as the peak to average ratio.

Smart meters migrate conventional electricity grid into digitally enabled Smart Grid (SG), which is more reliable and efficient. Fine-grained energy consumption data collected by smart meters helps utility providers accurately predict users' demands and significantly reduce power generation cost, while it imposes severe privacy risks on consumers and may discourage them from using those “espionage meters". To enjoy the benefits of smart meter measured data without compromising the users' privacy, in this paper, we try to integrate distributed differential privacy (DDP) techniques into data-driven optimization, and propose a novel scheme that not only minimizes the cost for utility providers but also preserves the DDP of users' energy profiles. Briefly, we add differential private noises to the users' energy consumption data before the smart meters send it to the utility provider. Due to the uncertainty of the users' demand distribution, the utility provider aggregates a given set of historical users' differentially private data, estimates the users' demands, and formulates the data- driven cost minimization based on the collected noisy data. We also develop algorithms for feasible solutions, and verify the effectiveness of the proposed scheme through simulations using the simulated energy consumption data generated from the utility company's real data analysis.

Elliptic curve cryptography (ECC) is an approach to public-key cryptography used for data protection to be unintelligible to any unauthorized device or entity. The encryption/decryption algorithm is publicly known and its security relies on the discrete logarithm problem. ECC is ideal for weak devices with small resources such as phones, smart cards, embedded systems and wireless sensor networks (WSN), largely deployed in different applications. The advantage of ECC is the shorter key length to provide same level of security than other cryptosystems like RSA. However, cryptographic computations such as the multiplication of an elliptic curve point by a scalar value are computationally expensive and involve point additions and doublings on elliptic curves over finite fields. Much works are done to optimize their costs. Based on the result of these works, including parallel processing, we propose two new efficient distributed algorithms to reduce the computations in resource-constrained networks having as feature the cooperative processing of data. Our results are conclusive and can provide up to 125% of reduction of consumed energy by each device in a data exchange operation.

Collaborative smart services provide functionalities which exploit data collected from different sources to provide benefits to a community of users. Such data, however, might be privacy sensitive and their disclosure has to be avoided. In this paper, we present a distributed multi-tier framework intended for smart-environment management, based on usage control for policy evaluation and enforcement on devices belonging to different collaborating entities. The proposed framework exploits secure multi-party computation to evaluate policy conditions without disclosing actual value of evaluated attributes, to preserve privacy. As reference example, a smart-grid use case is presented.

The increasing deployment of smart meters at individual households has significantly improved people's experience in electricity bill payments and energy savings. It is, however, still challenging to guarantee the accurate detection of attacked meters' behaviors as well as the effective preservation of users'privacy information. In addition, rare existing research studies jointly consider both these two aspects. In this paper, we propose a Privacy-Preserving energy Theft Detection scheme (PPTD) to address the energy theft behaviors and information privacy issues in smart grid. Specifically, we use a recursive filter based on state estimation to estimate the user's energy consumption, and detect the abnormal data. During data transmission, we use the lightweight NTRU algorithm to encrypt the user's data to achieve privacy preservation. Security analysis demonstrates that in the PPTD scheme, only authorized units can transmit/receive data, and data privacy are also preserved. The performance evaluation results illustrate that our PPTD scheme can significantly reduce the communication and computation costs, and effectively detect abnormal users.

Deep neural networks (DNNs) are effective machine learning models to solve a large class of recognition problems, including the classification of nonlinearly separable patterns. The applications of DNNs are, however, limited by the large size and high energy consumption of the networks. Recently, stochastic computation (SC) has been considered to implement DNNs to reduce the hardware cost. However, it requires a large number of random number generators (RNGs) that lower the energy efficiency of the network. To overcome these limitations, we propose the design of an energy-efficient deep belief network (DBN) based on stochastic computation. An approximate SC activation unit (A-SCAU) is designed to implement different types of activation functions in the neurons. The A-SCAU is immune to signal correlations, so the RNGs can be shared among all neurons in the same layer with no accuracy loss. The area and energy of the proposed design are 5.27% and 3.31% (or 26.55% and 29.89%) of a 32-bit floating-point (or an 8-bit fixed-point) implementation. It is shown that the proposed SC-DBN design achieves a higher classification accuracy compared to the fixed-point implementation. The accuracy is only lower by 0.12% than the floating-point design at a similar computation speed, but with a significantly lower energy consumption.

Internet of things has become a subject of interest across a different industry domain. It includes 6LoWPAN (Low-Power Wireless Personal Area Network) which is used for a variety of application including home automation, sensor networks, manufacturing and industry application etc. However, gathering such a huge amount of data from such a different domain causes a problem of traffic congestion, high reliability, high energy efficiency etc. In order to address such problems, content based routing (CBR) technique is proposed, where routing paths are decided according to the type of content. By routing the correlated data to hop nodes for processing, a higher data aggregation ratio can be obtained, which in turns reducing the traffic congestion and minimizes the energy consumption. CBR is implemented on top of existing RPL (Routing Protocol for Low Power and Lossy network) and implemented in contiki operating system using cooja simulator. The analysis are carried out on the basis average power consumption, packet delivery ratio etc.

As wireless mesh networks (WMNs) develop rapidly, security issue becomes increasingly important. Intrusion Detection System (IDS) is one of the crucial ways to detect attacks. However, IDS in wireless networks including WMNs brings high detection overhead, which degrades network performance. In this paper, we apply compressed sensing (CS) theory to IDS and propose a CS based IDS for hybrid WMNs. Since CS can reconstruct a sparse signal with compressive sampling, we process the detected data and construct sparse original signals. Through reconstruction algorithm, the compressive sampled data can be reconstructed and used for detecting intrusions, which reduces the detection overhead. We also propose Active State Metric (ASM) as an attack metric for recognizing attacks, which measures the activity in PHY layer and energy consumption of each node. Through intensive simulations, the results show that under 50% attack density, our proposed IDS can ensure 95% detection rate while reducing about 40% detection overhead on average.

In typical Wireless Sensor Network (WSN) applications, the sensor nodes deployed are constrained both in computational and energy resources. For this reason, simple communication protocols are usually employed along with shortrange multi-hop topologies. In this paper, we challenge this notion and propose a structure that employs more robust (and naturally more complex) forward-error correction schemes in multi-hop extended star topologies. We demonstrate using simulation and real-world data based on popular WSN platforms that this approach can actually reduce the overall energy consumption of the nodes by significant margins (from 40 to 70%) compared to traditional WSN schemes that do not support sophisticated communication mechanisms and it is feasible to implement it economically without relying on expensive hardware.

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.

The need for security in today's world has become a mandatory issue to look after. With the increase in a number of thefts, it has become a necessity to implement a smart security system. Due to the high cost of the existing smart security systems which use conventional Bluetooth and other wireless technologies and their relatively high energy consumption, implementing a security system with low energy consumption at a low cost has become the need of the hour. The objective of the paper is to build a cost effective and low energy consumption security system using the Bluetooth Low Energy (BLE) technology. This system will help the user to monitor and manage the security of the house even when the user is outside the house with the help of webpage. This paper presents the design and implementation of a security system using PSoC 4 BLE which can automatically lock and unlock the door when the user in the vicinity and leaving the vicinity of the door respectively by establishing a wireless connection between the physical lock and the smartphone. The system also captures an image of a person arriving at the house and transmits it wirelessly to a webpage. The system also notifies the user of any intrusion by sending a message and the image of the intruder to the webpage. The user can also access the door remotely on the go from the website.

Most of Wireless Sensor Networks (WSNs) are usually deployed in hostile environments where the communications conditions are not stable and not reliable. Hence, there is a need to design an effective distributed schemes to enable the sensors cooperating in order to recover the sensed data. In this paper, we establish a novel cooperative data exchange (CDE) scheme using instantly decodable network coding (IDNC) across the sensor nodes. We model the problem using the cooperative game theory in partition form. We develop also a distributed merge-and-split algorithm in order to form dynamically coalitions that maximize their utilities in terms of both energy consumption and IDNC delay experienced by all sensors. Indeed, the proposed algorithm enables these sensors to self-organize into stable clustered network structure where all sensors do not have incentives to change the cluster he is part of. Simulation results show that our cooperative scheme allows nodes not only to reduce the energy consumption, but also the IDNC completion time.

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/.

Embedded electronic devices and sensors such as smartphones, smart watches, medical implants, and Wireless Sensor Nodes (WSN) are making the “Internet of Things” (IoT) a reality. Such devices often require cryptographic services such as authentication, integrity and non-repudiation, which are provided by Public-Key Cryptography (PKC). As these devices are severely resource-constrained, choosing a suitable cryptographic system is challenging. Pairing Based Cryptography (PBC) is among the best candidates to implement PKC in lightweight devices. In this research, we present a fast and energy efficient implementation of PBC based on Barreto-Naehrig (BN) curves and optimal Ate pairing using hardware/software co-design. Our solution consists of a hardware-based Montgomery multiplier, and pairing software running on an ARM Cortex A9 processor in a Zynq-7020 System-on-Chip (SoC). The multiplier is protected against simple power analysis (SPA) and differential power analysis (DPA), and can be instantiated with a variable number of processing elements (PE). Our solution improves performance (in terms of latency) over an open-source software PBC implementation by factors of 2.34 and 2.02, for 256- and 160-bit field sizes, respectively, as measured in the Zynq-7020 SoC.

In Sybil attacks, a physical adversary takes multiple fabricated or stolen identities to maliciously manipulate the network. These attacks are very harmful for Internet of Things (IoT) applications. In this paper we implemented and evaluated the performance of RPL (Routing Protocol for Low-Power and Lossy Networks) routing protocol under mobile sybil attacks, namely SybM, with respect to control overhead, packet delivery and energy consumption. In SybM attacks, Sybil nodes take the advantage of their mobility and the weakness of RPL to handle identity and mobility, to flood the network with fake control messages from different locations. To counter these type of attacks we propose a trust-based intrusion detection system based on RPL.

The majority of available wearable computing devices require communication with Internet servers for data analysis and storage, and rely on a paired smartphone to enable secure communication. However, many wearables are equipped with WiFi network interfaces, enabling direct communication with the Internet. Secure communication protocols could then run on these wearables themselves, yet it is not clear if they can be efficiently supported.,,,,In this paper, we show that wearables are ready for direct and secure Internet communication by means of experiments with both controlled local web servers and Internet servers. We observe that the overall energy consumption and communication delay can be reduced with direct Internet connection via WiFi from wearables compared to using smartphones as relays via Bluetooth. We also show that the additional HTTPS cost caused by TLS handshake and encryption is closely related to the number of parallel connections, and has the same relative impact on wearables and smartphones.

6L0WPAN is a communication protocol for Internet of Things. 6LoWPAN is IPv6 protocol modified for low power and lossy personal area networks. 6LoWPAN inherits threats from its predecessors IPv4 and IPv6. IP spoofing is a known attack prevalent in IPv4 and IPv6 networks but there are new vulnerabilities which creates new paths, leading to the attack. This study performs the experimental study to check the feasibility of performing IP spoofing attack on 6LoWPAN Network. Intruder misuses 6LoWPAN control messages which results into wrong IPv6-MAC binding in router. Attack is also simulated in cooja simulator. Simulated results are analyzed for finding cost to the attacker in terms of energy and memory consumption.

In the recent years, Wireless Sensor Networks (WSN) and its applications have obtained considerable momentum. However, security and power limits of these networks are still important matters as security and power limits remain an important problem in WSN. This paper contributes to provide a simulation-based analysis of the energy efficiency, accuracy and path length of static and dynamic wireless sensor networks for 5G environment. Results are analyzed and discussed to show the difference between these two types of sensor networks. The static networks more accurate than dynamic networks. Data move from source to destination in shortest path in dynamic networks compared to static ones.

In the recent years, Wireless Sensor Networks (WSN) and its applications have obtained considerable momentum. However, security and power limits of these networks are still important matters as security and power limits remain an important problem in WSN. This paper contributes to provide a simulation-based analysis of the energy efficiency, accuracy and path length of static and dynamic wireless sensor networks for 5G environment. Results are analyzed and discussed to show the difference between these two types of sensor networks. The static networks more accurate than dynamic networks. Data move from source to destination in shortest path in dynamic networks compared to static ones.

The chips in working state have electromagnetic energy leakage problem. We offer a method to analyze the problem of electromagnetic leakage when the chip is running. We execute a sequence of addition and subtraction arithmetic instructions on FPGA chip, then we use the near-field probe to capture the chip leakage of electromagnetic signals. The electromagnetic signal is collected for analysis and processing, the parts of addition and subtraction are classified and identified by SVM. In this paper, for the problem of electromagnetic leakage, six sets of data were collected for analysis and processing. Good results were obtained by using this method.

Hierarchical based formation is one of the approaches widely used to minimize the energy consumption in which node with higher residual energy routes the data gathered. Several hierarchical works were proposed in the literature with two and three layered architectures. In the work presented in this paper, we propose an enhanced architecture for three layered hierarchical clustering based approach, which is referred to as enhanced three-layer hierarchical clustering approach (EHCA). The EHCA is based on an enhanced feature of the grid node in terms of its mobility. Further, in our proposed EHCA, we introduce distributed clustering technique for lower level head selection and incorporate security mechanism to detect the presence of any malicious node. We show by simulation results that our proposed EHCA reduces the energy consumption significantly and thus improves the lifetime of the network. Also, we highlight the appropriateness of the proposed EHCA for battlefield surveillance applications.