Resource-limited Sensor Research Articles

A Collaborative Planning Method of Space-Ground Sensor Network Coverage Optimization for Multiparameter Observation Tasks

A multiparameter observation task includes a comprehensive theme with multiple indispensable parameters that need to be monitored simultaneously. However, with limited observation sensor resources for a multiparameter observation task, the spatial misalignment of the coverage area of each parameter decreases the observation efficiency, especially in a space-ground sensor network. To solve this problem, we developed a collaborative planning method in the sensor planning phase. With this method, a space-ground maximal coverage model with multiple parameters (SGMC-MP) was introduced. The proposed collaborative planning method cooperatively utilizes the space-ground sensors to make an observation plan. This method aims to maximize the overlay coverage range among the parameters in the task to reduce the spatial misalignment and improve the utilization of the sensors. The proposed method was applied to a multiparameter observation task in the Three Gorges Reservoir Area in Chongqing. The results indicate that the proposed method exhibits better coverage performance for sensor planning in the multiparameter observation task than the traditional separate planning method. In addition, planning strategies, coverage flexibility, model extension, and algorithm comparison are further discussed.

Semidefinite Relaxation for Source Localization With Quantized ToA Measurements and Transmission Uncertainty in Sensor Networks

Accurate location information is critical for many engineering applications (e.g., radar, sonar, autonomous robots, intelligent transportation systems). In traditional source localization algorithms, the perfect knowledge of noisy Time-of-Arrival (ToA) measurements are assumed to be obtained by the fusion center in a sensor network. This assumption is not practical for wireless sensor networks, especially for a resource-limited sensor network with stringent power and communication bandwidth constraints. In this paper, we propose a novel channel-aware source localization method based on quantized asynchronous ToA measurements, where the quantization errors as well as the imperfect communication link between each sensor and the fusion center are considered. The maximum-likelihood (ML) source localization by jointly estimating the signal transmission instant and source location is formulated. An efficient relaxation is provided to transform the non-convex ML optimization problem into a convex problem. The Cramér-Rao lower bounds (CRLBs) for the quantized ToA measurements with the uncertainty of data exchange are derived. Furthermore, a Fisher information based heuristic quantization scheme is proposed to design quantized thresholds for asynchronous ToA measurements. The simulation and experimental results demonstrate that our proposed method can yield an efficient estimate under different scenarios.

EBDS: An energy-efficient big data-based secure framework using Internet of Things for green environment

The Internet of Things (IoT) has a rapid growth in the development of smart and green environments. The IoT-based physical objects are interconnected with sensors, actuators, and data centers to gather, process and store the environmental data. The collected data is further utilized for post analysis and smarter decisions by the Base Station (BS). However, the limited resources of IoT-based sensors in terms of energy, processing, and transmission power place pressure on the development of green societies. Moreover, the IoT-based network also connects with big data using the Internet to manipulate and store a huge amount of data on the cloud. Therefore, along with the decreasing of energy consumption, security, and data integrity is other research concerns. Accordingly, an energy-efficient and big data-based secure framework using the Internet of Things for a green environment is presented. Firstly, the IoT-based sensors are connecting for data gathering and perform data routing using the Dijkstra-based optimal algorithm. Such a proposed algorithm imposes lower overheads and minimizes energy consumption in finding the most reliable and least transmission distance routes. Secondly, it also secures the generated big data from network attackers and maintains the consistency of the green environment. The experimental results reveal the EBDS framework increases the performance of green environment for energy consumption by 16%, packet drop ratio by 33%, network throughput by 13%, end-to-end delay by 15.5%, and route stability by 16% in non-uniform data traffic as compared to other work.

Implementation of key predistribution scheme in WSN based on binary Goppa codes and Reed Solomon codes with enhanced connectivity and resiliency

Establishing secure transmissions of messages among the resource limited sensor nodes in wireless sensor network (WSN) is a critical issue and requires secret keys to be established among the communicating nodes. Key predistribution is the most commonly used technique and whereby secret keys are preloaded to the sensor nodes before their deployment into a hostile region. A WSN can be structured or unstructured, and sensor nodes may be deployed in an ad-hoc manner or pre-planned manner into the target field. As sensor nodes are low-cost electronic devices equipped with data processing, limited storage, communication and computation power, connectivity, and resiliency are the major focus in designing key predistribution scheme (KPS) for WSNs. Furthermore, we also expect the KPS to be scalable, enabling insertion of a set of new nodes in WSN at any point of time without altering the key setup of the already existing nodes. Combinatorial design is a widely used mathematical tool for the KPS. However, most of the KPS developed by using combinatorial design are not scalable. In this article, rather than using combinatorial techniques, we employ a code-based approach and design a new method for key predistribution by building a communication model and a connectivity model. We exploit the Reed Solomon code to establish our communication model, integrate the binary Goppa code to derive our connectivity model, and skillfully blend these two models to construct our code-based KPS. A C implementation of our KPS confirms the significant performance gain over the existing similar works. Additionally, nodes in our KPS are all self-dependent for communication and do not rely on cluster heads. Furthermore, we have control over the choice of the parameters in the underlying codes which makes our KPS flexible. To be specific, prior knowledge of additional node deployment increases the scalability of our connectivity model. By suitably choosing the parameters of the Goppa code at prior, we can accommodate extra nodes. More interestingly, our communication model is scalable without any previous knowledge of deployment.

A Matching Game-Based Data Collection Algorithm with Mobile Collectors.

Data collection is one of the key technologies in wireless sensor networks. Due to the limited battery resources of sensors, mobile collectors are introduced to collect data instead of multi-hop data relay. However, how to decrease the data delay based on the cooperation of mobile collectors is a main problem. To solve this problem, a matching game-based data collection algorithm is proposed. First, some high-level cluster heads are elected. Second, by introducing a matching game model, the data collection problem is modeled as a one to one matching problem. Then, according to the preferences of mobile collectors and cluster heads, the benefit matrices are established. Based on the proposed matching algorithm, each mobile collector selects a cluster head to collect the data packets. Performance analysis proves that the matching result is stable, optimal, and unique. Simulation results show that the proposed algorithm is superior to other existing approach in terms of the reduction in data delay.

Power Efficient Random Access for Massive NB-IoT Connectivity.

Sensors enabled Internet of things (IoT) has become an integral part of the modern, digital and connected ecosystem. Narrowband IoT (NB-IoT) technology is one of its economical versions preferable when low power and resource limited sensors based applications are considered. One of the major characteristics of NB-IoT technology is its offer of reliable coverage enhancement (CE) which is achieved by repeating the transmission of signals. This repeated transmission of the same signal challenges power saving in low complexity NB-IoT devices. Additionally, the NB-IoT devices are expected to suffer from congestion due to simultaneous random access procedures (RAPs) from an enormous number of devices. Multiple RAP reattempts would further reduce the power saving in NB-IoT devices. We propose a novel power efficient RAP (PE-RAP) for reducing power consumption of NB-IoT devices in a highly congested environment. The existing RAP do not differentiate the failures due to poor channel conditions or due to collision. After the RAP failure either due to collision or poor channel, the devices can apply power ramping or can transit to a higher CE level with higher repetition configuration. In the proposed PE-RAP, the NB-IoT devices can re-ascertain the channel conditions after an RAP attempt failure such that the impediments due to poor channel are reduced. The power increments and repetition enhancements are applied only when necessary. We probabilistically obtain the chances of RAP reattempts. Subsequently, we evaluate the average power consumption by devices in different CE levels for different repetition configurations. We validate our analysis by simulation studies.

Attitude Control of a Small Spacecraft via Tube-Based Model Predictive Control

Precise attitude tracking control of small satellites is a demanding task due to the limited hardware resources of sensors, actuators, and processors equipped on board. Moreover, the effects of model uncertainty and persistent disturbance, such as gravity-gradient, magnetic, aerodynamic, and solar radiation pressure torques, on the satellite dynamics make the attitude control of miniaturized spacecraft more challenging. Consequently, under actuator limitations and stringent pointing requirements, robust controllers represent a more reliable solution to comply the mission constraints. In this paper, a tube-based robust model predictive control algorithm is exploited to address robust attitude control of CubeSat within an Earth-observation scenario, and its performance is evaluated. The proposed algorithm successfully achieves the three-axis stabilization required by the payload, using reaction wheels and applying constraints on the actuator torque and speed saturation as well as on pointing accuracy. To properly estimate the effectiveness of the proposed approach, the comparison with a nonlinear model predictive control is presented with the consideration of different sample times. The robustness of these two control schemes is then compared running 100 simulations, each one with random initial condition. The results demonstrate the effectiveness of the proposed robust algorithm for precise attitude tracking control of small satellites.

Efficient Hierarchical Identity-Based Encryption System for Internet of Things Infrastructure

Security is a main concern for the Internet of Things (IoT) infrastructure as large volumes of data are collected and processed in the systems. Due to the limited resources of interconnected sensors and devices in the IoT systems, efficiency is one of the key considerations when deploying security solutions (e.g., symmetric/asymmetric encryption, authentication, etc.) in IoT. In this paper, we present an efficient Hierarchical Identity-Based Encryption (HIBE) system with short parameters for protecting data confidentiality in distributed IoT infrastructure. Our proposed HIBE system has the public parameters, private key, and ciphertext, each consisting of a constant number of group elements. We prove the full security of the HIBE system in the standard model using the dual system encryption technique. We also implement the proposed scheme and compare the performance with the original Lewko–Waters HIBE. To the best of our knowledge, our construction is the first HIBE system that achieves both full security in the standard model and short parameters in terms of the public parameters, private key, and ciphertext.

Computationally efficient mutual authentication protocol for remote infant incubator monitoring system.

Internet of Things (IoT), cloud computing and wireless medical sensor networks have significantly improved remote healthcare monitoring. In a healthcare monitoring system, many resource-limited sensors are deployed to sense, process and communicate the information. However, continuous and accurate operations of these devices are very important, especially in the infant incubator monitoring system. Because important decisions are made on the received information. Therefore, it is necessary to ensure the authenticity between the incubator monitoring system and doctors. In this work, a public key encryption based computationally efficient mutual authentication protocol is proposed for secure data transmission between incubator monitoring systems and doctors or administrators. The proposed protocol improves performance and reduces the computational cost without compromising the security. The security analysis part shows the strength of the proposed protocol against various attacks, performance analysis part shows that the proposed protocol performs better than other existing protocol based on Rivest–Shamir–Adleman and elliptic-curve cryptography schemes.

An efficient load balancing and performance optimization scheme for constraint oriented networks

A constraint oriented wireless sensor network (WSN) faces a challenging task of handling different issues i.e., routing mechanism, coverage of the underlined sensing fields for as long as possible, energy efficiency, reliable transmission of packets, congestion controls etc. Load balancing or routing protocols, which are specifically designed for the resource limited sensor networks, resolve some of these issues. However, most of these mechanisms were either applications specific or overlay complex. Moreover, these techniques underestimated an important aspect of WSNs i.e., sensor nodes criticality or vulnerability factor that is defined as the importance of a node from the network connectivity perspective. In this paper, an efficient load balancing and performance optimization scheme (LBS) is presented to address these issues such as throughput, long term connectivity, end to end delay and energy efficiency in resource limited WSNs. The proposed LBS technique conflates a sensor node criticality factor, residual energy and hop-count to form an optimized load balancing strategy. Initially, the proposed LBS routes most of the load or traffic on the shortest paths by assigning a highest weight-age factor, 80%, to the hop count metric and lower weight-age to both criticality and residual energy metrics i.e., 20%. Load balancing strategy is revised if one or more nodes deplete 50% of their on-board batteries. Simulation results verify that the proposed LBS scheme outperforms the existing field proven approaches i.e., shortest path, vulnerability aware routing, energy based spreading and multiple path based load balancing schemes.

AQ-Routing: mobility-, stability-aware adaptive routing protocol for data routing in MANET–IoT systems

Internet of Things, is an innovative technology which allows the connection of physical things with the digital world through the use of heterogeneous networks and communication technologies. In an IoT system, a major role is played by the wireless sensor network as its components comprise: sensing, data acquiring, heterogeneous connectivity and data processing. Mobile ad-hoc networks are highly self reconfiguring networks of mobile nodes which communicate through wireless links. In such a network, each node acts both as a router and host at the same time. The interaction between MANETs and Internet of Things opens new ways for service provision in smart environments and challenging issues in its networking aspects. One of the main issues in MANET–IoT systems is the mobility of the network nodes: routing protocol must react effectively to the topological changes into the algorithm design. We describe the design and implementation of AQ-Routing, and analyze its performance using both simulations and measurements based on our implementation. In general, the networking of such a system is very challenging regarding routing aspects. Also, it is related to system mobility and limited network sensor resources. This article builds upon this observation an adaptive routing protocol (AQ-Routing) based on Reinforcement Learning (RL) techniques, which has the ability to detect the level of mobility at different points of time so that each individual node can update routing metric accordingly. The proposed protocol introduces: (i) new model, developed via Q-learning technique, to detect the level of mobility at each node in the network; (ii) a new metric, called $$Q_{\textit{metric}},$$ which account for the static and dynamic routing metrics, and which are combined and updated to the changing network topologies. The protocol can efficiently handle network mobility by a way of preemptively adapting its behaviour thanks to the mobility detection model. The presented results of simulation provide an effective approach to improve the stability of links in both static and mobile scenario and, hence, increase the packet delivery ratio in the global MANET–IoT system.

Capacity-Outage Joint Analysis and Optimal Power Allocation for Wireless Body Area Networks

Wireless body area networks (WBANs) are characterized by short-range wireless communication between inherently resource-limited sensors that operate in the vicinity of the human body for recording certain physiological signals. One of the main limitations of WBANs is in terms of their available power. Since the nodes are typically battery-driven, an efficient power transmission is essential to guarantee long-lasting communications among nodes without the need for frequent battery replacement or charging. In this study, aiming to improve the WBAN performance, we first investigate the power allocation problem by choosing the ergodic capacity and the outage probability as the desired metrics. Subject to an average power constraint, it can be shown that the power allocation, which is optimal for the ergodic capacity, does not necessarily yield a good result for the outage probability, and vice versa. Therefore, we assert that although both of the aforementioned metrics are recognized as the key performance indicators, the WBAN performance would not be appropriately characterized by considering each of them separately. Consequently, a novel hybrid metric is proposed that jointly accounts for the capacity and the outage. Subject to an average power constraint, we then find the power allocation policy that maximizes the proposed hybrid metric. The entire devised framework is utilized in the context of WBAN channels derived from applying the well-known Bayesian information criterion to a set of publicly available experimental channel data. The numerical results demonstrate the superiority of the proposed power allocation policy compared to the legacy water-filling and truncated-inversion approaches.

LACO: Lightweight Three-Factor Authentication, Access Control and Ownership Transfer Scheme for E-Health Systems in IoT

The use of the Internet of Things (IoT) in the electronic health (e-health) management systems brings with it many challenges, including secure communications through insecure radio channels, authentication and key agreement schemes between the entities involved, access control protocols and also schemes for transferring ownership of vital patient information. Besides, the resource-limited sensors in the IoT have real difficulties in achieving this goal. Motivated by these considerations, in this work we propose a new lightweight authentication and ownership transfer protocol for e-health systems in the context of IoT (LACO in short). The goal is to propose a secure and energy-efficient protocol that not only provides authentication and key agreement but also satisfies access control and preserves the privacy of doctors and patients. Moreover, this is the first time that the ownership transfer of users is considered. In the ownership transfer phase of the proposed scheme, the medical server can change the ownership of patient information. In addition, the LACO protocol overcomes the security flaws of recent authentication protocols that were proposed for e-health systems, but are unfortunately vulnerable to traceability, de-synchronization, denial of service (DoS), and insider attacks. To avoid past mistakes, we present formal (i.e., conducted on ProVerif language) and informal security analysis for the LACO protocol. All this ensures that our proposed scheme is secure against the most common attacks in IoT systems. Compared to the predecessor schemes, the LACO protocol is both more efficient and more secure to use in e-health systems.

Sensor management of LEO constellation using modified binary particle swarm optimization

In this paper, we are concerned with the problem of utilizing a low Earth orbit (LEO) infrared constellation in order to track the midcourse ballistic missile. Due to the limited sensor resources of LEO constellation, an appropriate sensor management strategy is necessary. The posterior Cramér-Rao lower bound (PCRLB) is used as the basis for sensor management of LEO constellation. Moreover, a modified binary particle swarm optimization (MBPSO) algorithm is presented for use in finding the optimal allocation scheme of sensors for maximizing the tracking accuracy. The proposed MBPSO algorithm adopts a different transfer function and a new method to generate initial population. The MBPSO’s performance and the PCRLB-based sensor management strategy are verified by applying to a case study.

Depletion-of-Battery Attack: Specificity, Modelling and Analysis.

The emerging Internet of Things (IoT) has great potential; however, the societal costs of the IoT can outweigh its benefits. To unlock IoT potential, there needs to be improvement in the security of IoT applications. There are several standardization initiatives for sensor networks, which eventually converge with the Internet of Things. As sensor-based applications are deployed, security emerges as an essential requirement. One of the critical issues of wireless sensor technology is limited sensor resources, including sensor batteries. This creates a vulnerability to battery-exhausting attacks. Rapid exhaustion of sensor battery power is not only explained by intrusions, but can also be due to random failure of embedded sensor protocols. Thus, most wireless sensor applications, without tools to defend against rash battery exhausting, would be unable to function during prescribed times. In this paper, we consider a special type of threat, in which the harm is malicious depletion of sensor battery power. In contrast to the traditional denial-of-service attack, quality of service under the considered attack is not necessarily degraded. Moreover, the quality of service can increase up to the moment of the sensor set crashes. We argue that this is a distinguishing type of attack. Hence, the application of a traditional defense mechanism against this threat is not always possible. Therefore, effective methods should be developed to counter the threat. We first discuss the feasibility of rash depletion of battery power. Next, we propose a model for evaluation of energy consumption when under attack. Finally, a technique to counter the attack is discussed.

Natural Timestamps in Powerline Electromagnetic Radiation

The continuous fluctuation of electric network frequency (ENF) presents a fingerprint indicative of time, which we call natural timestamp . This article studies the time accuracy of these natural timestamps obtained from powerline electromagnetic radiation (EMR), which is mainly excited by powerline voltage oscillations at the rate of the ENF. However, since the EMR signal is often weak and noisy, extracting the ENF is challenging, especially on resource-limited sensor platforms. We design an efficient EMR conditioning algorithm and evaluate the time accuracy of EMR natural timestamps on two representative classes of IoT platforms—a high-end single-board computer with a customized EMR antenna and a low-end mote with a normal conductor wire acting as EMR antenna. Extensive measurements at six sites in a city, which are away from each other for up to 24km, show that the high-end and low-end nodes achieve median time errors of about 50ms and 150ms, respectively. To demonstrate the use of the EMR natural timestamps, we discuss three applications: time recovery, runtime clock verification, and secure clock synchronization.

On Applicability of Network Coding Technique for 6LoWPAN-based Sensor Networks.

In this paper, the applicability of the network coding technique in 6LoWPAN-based sensor multihop networks is examined. The 6LoWPAN is one of the standards proposed for the Internet of Things architecture. Thus, we can expect the significant growth of traffic in such networks, which can lead to overload and decrease in the sensor network lifetime. The authors propose the inter-session network coding mechanism that can be implemented in resource-limited sensor motes. The solution reduces the overall traffic in the network, and in consequence, the energy consumption is decreased. Used procedures take into account deep header compressions of the native 6LoWPAN packets and the hop-by-hop changes of the header structure. Applied simplifications reduce signaling traffic that is typically occurring in network coding deployments, keeping the solution usefulness for the wireless sensor networks with limited resources. The authors validate the proposed procedures in terms of end-to-end packet delay, packet loss ratio, traffic in the air, total energy consumption, and network lifetime. The solution has been tested in a real wireless sensor network. The results confirm the efficiency of the proposed technique, mostly in delay-tolerant sensor networks.

Development and experimental validation of a multi-algorithmic hybrid attitude determination and control system for a small satellite

Advanced missions of satellites are increasingly demanding more accurate and robust attitude maneuvering capabilities. However, it is difficult to achieve especially for small satellites due to limited hardware resources of sensors, actuators, and processors. In this paper, to achieve the desired performance, a multi-algorithmic hybrid attitude determination and control system (ADCS) that utilizes a family of control and estimation algorithms is developed and implemented in numerical simulations and experiments for a small satellite. The hybrid automaton framework of the ADCS is designed to accomplish the desired performance with the limited hardware capability by switching the control and estimation algorithms effectively for given situations in space. The performance of the hybrid ADCS is evaluated through numerical and hardware-in-the-loop simulations that are based on a three-dimensional air-bearing testbed, CubeSat Three-Axis Simulator (CubeTAS). Simulation and experimental results demonstrate the effectiveness of the multi-algorithmic hybrid ADCS. The significance of this paper is in demonstrating that the hybrid automaton framework can be an effective approach to handle operational situations in space. It also provides a design reference for a small satellite ADCS.

Compact Hardware Implementation of a SHA-3 Core for Wireless Body Sensor Networks

One of the most important Internet of Things applications is the wireless body sensor network (WBSN), which can provide universal health care, disease prevention, and control. Due to large deployments of small scale smart sensors in WBSNs, security, and privacy guarantees (e.g., security and safety-critical data, sensitive private information) are becoming a challenging issue because these sensor nodes communicate using an open channel, i.e., Internet. We implement data integrity (to resist against malicious tampering) using the secure hash algorithm 3 (SHA-3) when smart sensors in WBSNs communicate with each other using the Internet. Due to the limited resources (i.e., storage, computation, and communication capabilities) of sensors in WBSNs, a lightweight implementation of SHA-3 is needed. To address this challenge, we propose a new implementation of the SHA-3, which has a compact hardware architecture. Our implementation of SHA-3 consists of a reliable logic structure, random access memory, and an enhanced finite state machine. The simulation on a Vitrtex-5 field programmable gate array shows that the proposed implementation is suitable for the WBSN on different applications. We evaluate the sensor area of the proposed SHA-3 implementation and compare it with other recently proposed hardware implementations of SHA-3. In addition, our hardware implementation approach reduces the area by almost 74.7% compared with the recently proposed hardware implementation which has the smallest area.

Lossless In-Network Processing and Its Routing Design in Wireless Sensor Networks

In many domain-specific monitoring applications of wireless sensor networks (WSNs), such as structural health monitoring, volcano tomography, and machine diagnosis, the raw data in WSNs are required to be losslessly gathered to the sink, where a specialized centralized algorithm is then executed to extract some global features or model parameters. To reduce the large raw data transmission, in-network processing is usually employed. However, different from most existing in-network processing works that pre-assume some common computation/aggregation functions, in-network processing of a given centralized algorithm requires exact partitioning of the algorithm first and then appropriately assigning the partitioned computations into WSNs. We call this lossless in-network processing, which has not been studied much. Lossless in-network processing raises two questions: 1) what pattern should a centralized algorithm be partitioned into so that the partitioned computations can be flexibly assigned into a WSN with arbitrary topology? and 2) for each partition pattern, how should efficient routing for the resource-limited sensor nodes be designed? These two questions can be referred to as a topology-constrained computation partition problem and a computation-constrained routing design problem, respectively. In this paper, we first introduce some general patterns on the topology-constrained computation partition. Then, with the computation constraints in the patterns, we present a series of novel routing schemes customized for different cases of computation results. The work in this paper can also serve as a guideline for distributed computing of big data, where the data spreads in a large network.