Neuromorphic hardware for somatosensory neuroprostheses

In individuals with sensory-motor impairments, missing limb functions can be restored using neuroprosthetic devices that directly interface with the nervous system. However, restoring the natural tactile experience through electrical neural stimulation requires complex encoding strategies. Indeed, they are presently limited in effectively conveying or restoring tactile sensations by bandwidth constraints. Neuromorphic technology, which mimics the natural behavior of neurons and synapses, holds promise for replicating the encoding of natural touch, potentially informing neurostimulation design. In this perspective, we propose that incorporating neuromorphic technologies into neuroprostheses could be an effective approach for developing more natural human-machine interfaces, potentially leading to advancements in device performance, acceptability, and embeddability. We also highlight ongoing challenges and the required actions to facilitate the future integration of these advanced technologies

Mobile sink based fault diagnosis scheme for wireless sensor networks

Network diagnosis in Wireless Sensor Networks (WSNs) is a difficult task due to their improvisational nature, invisibility of internal running status, and particularly since the network structure can frequently change due to link failure. To solve this problem, we propose a Mobile Sink (MS) based distributed fault diagnosis algorithm for WSNs. An MS, or mobile fault detector is usually a mobile robot or vehicle equipped with a wireless transceiver that performs the task of a mobile base station while also diagnosing the hardware and software status of deployed network sensors. Our MS mobile fault detector moves through the network area polling each static sensor node to diagnose the hardware and software status of nearby sensor nodes using only single hop communication. Therefore, the fault detection accuracy and functionality of the network is significantly increased. In order to maintain an excellent Quality of Service (QoS), we employ an optimal fault diagnosis tour planning algorithm. In addition to saving energy and time, the tour planning algorithm excludes faulty sensor nodes from the next diagnosis tour. We demonstrate the effectiveness of the proposed algorithms through simulation and real life experimental results

Software Defined Networks based Smart Grid Communication: A Comprehensive Survey

The current power grid is no longer a feasible solution due to ever-increasing user demand of electricity, old infrastructure, and reliability issues and thus require transformation to a better grid a.k.a., smart grid (SG). The key features that distinguish SG from the conventional electrical power grid are its capability to perform two-way communication, demand side management, and real time pricing. Despite all these advantages that SG will bring, there are certain issues which are specific to SG communication system. For instance, network management of current SG systems is complex, time consuming, and done manually. Moreover, SG communication (SGC) system is built on different vendor specific devices and protocols. Therefore, the current SG systems are not protocol independent, thus leading to interoperability issue. Software defined network (SDN) has been proposed to monitor and manage the communication networks globally. This article serves as a comprehensive survey on SDN-based SGC. In this article, we first discuss taxonomy of advantages of SDNbased SGC.We then discuss SDN-based SGC architectures, along with case studies. Our article provides an in-depth discussion on routing schemes for SDN-based SGC. We also provide detailed survey of security and privacy schemes applied to SDN-based SGC. We furthermore present challenges, open issues, and future research directions related to SDN-based SGC.Comment: Accepte

Evolving attackers against wireless sensor networks using genetic programming

Recent hardware developments have made it possible for the Internet of Things (IoT) to be built. A wide variety of industry sectors, including manufacturing, utilities, agriculture, transportation, and healthcare are actively seeking to incorporate IoT technologies in their operations. The increased connectivity and data sharing that give IoT systems their advantages also increase their vulnerability to attack. In this study, the authors explore the automated generation of attacks using genetic programming (GP), so that defences can be tested objectively in advance of deployment. In the authors' system, the GP-generated attackers targeted publish-subscribe communications within a wireless sensor networks that was protected by an artificial immune intrusion detection system (IDS) taken from the literature. The GP attackers successfully suppressed more legitimate messages than the hand-coded attack used originally to test the IDS, whilst reducing the likelihood of detection. Based on the results, it was possible to reconfigure the IDS to improve its performance. Whilst the experiments were focussed on establishing a proof-of-principle rather than a turnkey solution, they indicate that GP-generated attackers have the potential to improve the protection of systems with large attack surfaces, in a way that is complementary to traditional testing and certification

Towards a Secure and Resilient Vehicle Design: Methodologies, Principles and Guidelines

The advent of autonomous and connected vehicles has brought new cyber security challenges to the automotive industry. It requires vehicles to be designed to remain dependable in the occurrence of cyber-attacks. A modern vehicle can contain over 150 computers, over 100 million lines of code, and various connection interfaces such as USB ports, WiFi, Bluetooth, and 4G/5G. The continuous technological advancements within the automotive industry allow safety enhancements due to increased control of, e.g., brakes, steering, and the engine. Although the technology is beneficial, its complexity has the side-effect to give rise to a multitude of vulnerabilities that might leverage the potential for cyber-attacks. Consequently, there is an increase in regulations that demand compliance with vehicle cyber security and resilience requirements that state vehicles should be designed to be resilient to cyber-attacks with the capability to detect and appropriately respond to these attacks. Moreover, increasing requirements for automotive digital forensic capabilities are beginning to emerge. Failures in automated driving functions can be caused by hardware and software failures as well as cyber security issues. It is imperative to investigate the cause of these failures. However, there is currently no clear guidance on how to comply with these regulations from a technical perspective.In this thesis, we propose a methodology to predict and mitigate vulnerabilities in vehicles using a systematic approach for security analysis; a methodology further used to develop a framework ensuring a resilient and secure vehicle design concerning a multitude of analyzed vehicle cyber-attacks. Moreover, we review and analyze scientific literature on resilience techniques, fault tolerance, and dependability for attack detection, mitigation, recovery, and resilience endurance. These techniques are then further incorporated into the above-mentioned framework. Finally, to meet requirements to hastily and securely patch the increasing number of bugs in vehicle software, we propose a versatile framework for vehicle software updates

A Test Environment for Wireless Hacking in Domestic IoT Scenarios

Security is gaining importance in the daily life of every citizen. The advent of Internet of Things devices in our lives is changing our conception of being connected through a single device to a multiple connection in which the centre of connection is becoming the devices themselves. This conveys the attack vector for a potential attacker is exponentially increased. This paper presents how the concatenation of several attacks on communication protocols (WiFi, Bluetooth LE, GPS, 433 Mhz and NFC) can lead to undesired situations in a domestic environment. A comprehensive analysis of the protocols with the identification of their weaknesses is provided. Some relevant aspects of the whole attacking procedure have been presented to provide some relevant tips and countermeasures.This work has been partially supported by the Spanish Ministry of Science and Innovation through the SecureEDGE project (PID2019-110565RB-I00), and by the by the Andalusian FEDER 2014-2020 Program through the SAVE project (PY18-3724). // Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. // Funding for open access charge: Universidad de Málaga / CBU

Recent advances in intelligent-based structural health monitoring of civil structures

This survey paper deals with the structural health monitoring systems on the basis of methodologies involving intelligent techniques. The intelligent techniques are the most popular tools for damage identification in terms of high accuracy, reliable nature and the involvement of low cost. In this critical survey, a thorough analysis of various intelligent techniques is carried out considering the cases involved in civil structures. The importance and utilization of various intelligent tools to be mention as the concept of fuzzy logic, the technique of genetic algorithm, the methodology of neural network techniques, as well as the approaches of hybrid methods for the monitoring of the structural health of civil structures are illustrated in a sequential manner

Edge Computing for Extreme Reliability and Scalability

The massive number of Internet of Things (IoT) devices and their continuous data collection will lead to a rapid increase in the scale of collected data. Processing all these collected data at the central cloud server is inefficient, and even is unfeasible or unnecessary. Hence, the task of processing the data is pushed to the network edges introducing the concept of Edge Computing. Processing the information closer to the source of data (e.g., on gateways and on edge micro-servers) not only reduces the huge workload of central cloud, also decreases the latency for real-time applications by avoiding the unreliable and unpredictable network latency to communicate with the central cloud

Mementos: System support for long-running computation on RFID-scale devices

Abstract Many computing systems include mechanisms designed to defend against sudden catastrophic losses of computational state, but few systems treat such losses as the common case rather than exceptional events. On the other end of the spectrum are transiently powered computing devices such as RFID tags and smart cards; these devices are typically paired with code that must complete its task under tight time constraints before running out of energy. Mementos is a software system that transforms general-purpose programs into interruptible computations that are protected from frequent power losses by automatic, energy-aware state checkpointing. Mementos comprises a collection of optimization passes for the LLVM compiler infrastructure and a linkable library that exercises hardware support for energy measurement while managing state checkpoints stored in nonvolatile memory. We evaluate Mementos against diverse test cases and find that, although it introduces time overhead of up to 60% in our tests versus uninstrumented code executed without power failures, it effectively spreads program execution across zero or more complete losses of power and state. Other contributions of this work include