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Recommendation systems are crucial for providing services to the elderly with Alzheimer’s disease in IoT-based smart home environments. Therefore, we present a Reminder Care System to help Alzheimer patients live safely and independently in their homes. The proposed recommendation system is formulated based on a contextual bandit approach to tackle dynamicity in human activity patterns for accurate recommendations meeting user needs without their feedback. Our experiment results demonstrate the feasibility and effectiveness of the proposed Reminder Care System in real-world IoT-based smart home applications.

Sharing credentials could raise privacy concerns. For digital credentials to be widely accepted, there is a need for an end-to-end system that provides (i) secure verification of the participant identities and credentials to increase trust, and (ii) a data minimisation mechanism to reduce the risk of oversharing the credential data. This paper proposes CredChain, a blockchain-based Self-Sovereign Identity (SSI) platform architecture that allows secure creation, sharing and verification of credentials. Beyond the verification of identities and credentials, a flexible selective disclosure solution is proposed using redactable signatures. The credentials are managed through a decentralised application/wallet which allows users to store their credential data privately under their full control and re-use as necessary. Our evaluation results show that CredChain architecture is feasible, secure and exhibits the level of performance that is within the expected benchmarks of the well-known blockchain platform, Parity Ethereum.

Deployment of the Internet of Things (IoT) in smart buildings has received considerable interest from both the academic community and commercial sectors. Unfortunately, the widespread adoption of current smart building solutions is inhibited by the high costs associated with installation and maintenance. Moreover, different types of IoT devices from different manufacturers typically form distinct networks and data silos. There is a need to use a common backbone network that facilitates interoperability and seamless data exchange in a uniform way. In this paper, we present EMIoT, a novel solution for smart buildings that breaks these barriers by leveraging existing emergency lighting systems. In EMIoT, we embed a wireless LoRa module in each emergency light to turn them into wireless routers. EMIoT has been deployed in more than 50 buildings of different types in Sydney, Australia and has been successfully running over two years. We present the design and implementation of EMIoT in this paper. Moreover, we use the deployment in a residential building as a use case to show the performance of EMIoT in real-world environments.

IoT devices are the target of choice for attackers, and one of the most devastating threats involving compromised IoT devices has been their exploitation as part of botnets. Here, we propose c-Shield, as a distributed and extensible solution designed to detect and respond to IoT-based bots in an enterprise network. c-Shield passively inspects network traffic associated with IoT devices over a range of different protocols and systematically analyses the URLs extracted. Compared with the existing solutions, c-Shield is designed to be capable of detecting bots using advanced evasion techniques such as Domain Name Generation Algorithms (DGA) with a high accuracy rate.

Mobile Crowdsensing (MCS) systems take advantage of the ubiquity and sensing power of smartphones in data gathering. Reverse auction is a popular incentive mechanism framework for MCS wherein, the participants can determine their expected rewards for their contributions. In this paper, for the first time, we consider a multi-task location-dependent reverse auction based MCS setting wherein, each task requires a specific amount of contribution to be fulfilled, participants may need to move to the task locations in order to participate in them, and the goal is to assign each participant to at most one task in a way such that the cumulative contribution of the fulfilled tasks are maximised while not exceeding a limited budget. We show that this is an NP-hard optimization problem and propose Ant Colony Optimisation-based Multi-Task Assignment (ACOMTA) as an approximation algorithm for it. We uncover an issue with the basic instantiation of ACO and propose an approach called Valid Random Path Generator (VRPG) to avoid lack of or premature convergence of the algorithm. Through extensive experiments, we show that the proposed algorithm outperforms a greedy approach as well as a random-based solution.

Targeted advertising has transformed the marketing landscape for a wide variety of businesses, by creating new opportunities for advertisers to reach prospective customers by delivering personalised ads, using an infrastructure of a number of intermediary entities and technologies. The advertising and analytics companies collect, aggregate, process and trade a vast amount of user's personal data, which has prompted serious privacy concerns among both individuals and organisations. This article presents a detailed survey of the associated privacy risks and proposed solutions in a mobile environment. We outline details of the information flow between the advertising platform and ad/analytics networks, the profiling process, advertising sources and criteria, the measurement analysis of targeted advertising based on user's interests and profiling context and the ads delivery process, for both in-app and in-browser targeted ads; we also include an overview of data sharing and tracking technologies. We discuss challenges in preserving user privacy that include threats related to private information extraction and exchange among various advertising entities, privacy threats from third-party tracking, re-identification of private information and associated privacy risks. Subsequently, we present various techniques for preserving user privacy and a comprehensive analysis of the proposals based on such techniques; we compare the proposals based on the underlying architectures, privacy mechanisms and deployment scenarios. Finally, we discuss the potential research challenges and open research issues.

We propose CrowdPatching, a blockchain-based decentralized protocol, allowing Internet of Things (IoT) manufacturers to delegate the delivery of software updates to self-interested distributors in exchange for cryptocurrency. Manufacturers announce updates by deploying a smart contract (SC), which in turn will issue cryptocurrency payments to any distributor who provides an unforgeable proof-of-delivery. The latter is provided by IoT devices authorizing the SC to issue payment to a distributor when the required conditions are met. These conditions include the requirement for a distributor to generate a zero-knowledge proof, generated with a novel proving system called zk-SNARKs. Compared with related work, CrowdPatching protocol offers three main advantages. First, the number of distributors can scale indefinitely by enabling the addition of new distributors at any time after the initial distribution by manufacturers (i.e., redistribution among the distributor network). The latter is not possible in existing protocols and is not account for. Secondly, we leverage the recent common integration of gateway or Hub in IoT deployments in our protocol to make CrowdPatching feasible even for the more constraint IoT devices. Thirdly, the trustworthiness of distributors is considered in our protocol, rewarding the honest distributors' engagements. We provide both informal and formal security analysis of CrowdPatching using Tamarin Prover., This is a copy of the accepted version at The 19th IEEE International Symposium on Network Computing and Applications (NCA 2020) [Core Rank: A]. The final version appearing in the conference proceedings will have additions and changes

The recent outbreak of a novel coronavirus and its rapid spread underlines the importance of understanding human mobility. Enclosed spaces, such as public transport vehicles (e.g. buses and trains), offer a suitable environment for infections to spread widely and quickly. Investigating the movement patterns and the physical encounters of individuals on public transit systems is thus critical to understand the drivers of infectious disease outbreaks. For instance previous work has explored the impact of recurring patterns inherent in human mobility on disease spread, but has not considered other dimensions such as the distance travelled or the number of encounters. Here, we consider multiple mobility dimensions simultaneously to uncover critical information for the design of effective intervention strategies. We use one month of citywide smart card travel data collected in Sydney, Australia to classify bus passengers along three dimensions, namely the degree of exploration, the distance travelled and the number of encounters. Additionally, we simulate disease spread on the transport network and trace the infection paths. We investigate in detail the transmissions between the classified groups while varying the infection probability and the suspension time of pathogens. Our results show that characterizing individuals along multiple dimensions simultaneously uncovers a complex infection interplay between the different groups of passengers, that would remain hidden when considering only a single dimension. We also identify groups that are more influential than others given specific disease characteristics, which can guide containment and vaccination efforts., 10 pages, 10 figures and 1 table. To be published in the 2020 21st IEEE International Symposium on A World of Wireless, Mobile and Multimedia Networks (IEEE WOWMOM 2020) conference program and the proceedings

Currently, blockchain proposals are being adopted to solve security issues, such as data integrity, resilience, and non-repudiation. To improve certain aspects, e.g., energy consumption and latency, of traditional blockchains, different architectures, algorithms, and data management methods have been recently proposed. For example, appendable-block blockchain uses a different data structure designed to reduce latency in block and transaction insertion. It is especially applicable in domains such as Internet of Things (IoT), where both latency and energy are key concerns. However, the lack of some features available to other blockchains, such as Smart Contracts, limits the application of this model. To solve this, in this work, we propose the use of Smart Contracts in appendable-block blockchain through a new model called context-based appendable-block blockchain. This model also allows the execution of multiple smart contracts in parallel, featuring high performance in parallel computing scenarios. Furthermore, we present an implementation for the context-based appendable-block blockchain using an Ethereum Virtual Machine (EVM). Finally, we execute this implementation in four different testbed. The results demonstrated a performance improvement for parallel processing of smart contracts when using the proposed model.

Blockchain-based approaches are increasingly being used to provide distributed trust and security in Distributed Energy Trading (DET). However, the state-of-the-art solutions lack scalability, privacy, interoperability, and often have large computational overheads hindering their mainstream adoption for sustainable development. To address these challenges, this paper proposes a multi-relationship network framework (RNF) that uses hypergraphs to organise participants in energy trading networks based on high-order relationships (rather than pairwise) for flexibility, interoperability, data privacy, and reduced resource consumption. Results indicate that the proposed framework outperforms the baseline in terms of: enabling value transfer across multiple blockchain-based DET systems; reducing computational costs and achieving energy efficiency for sustainable development.

We propose a technological framework based on the combination of the Internet of Things (IoT) and Blockchains aiming at incentivizing and rewarding more sustainable water management practices in agriculture. In this context, current IoT-based precision agriculture deployments prefer energy efficiency, which generally translates into power-and-resource-constrained sensing devices. For this reason, often, system integrators of this sector feel the need to interpose third-party hardware intermediaries (e.g., IoT gateways) between sensing devices and blockchain endpoints, so augmenting infrastructural costs and reducing the trustworthiness of the data acquired from the field. In this paper, we present a software architecture specifically designed for a trustless water management system where constrained IoT devices can directly transact sensed data on a public blockchain network. We deploy the proposed solution on off-the-shelf hardware devices and undertake a thorough benchmarking in terms of memory, program size, communication overheads and power consumption. Our results show that, in general, typical IoT devices can be used to directly interact with a blockchain, without severe burden. More specifically, these devices only incur an additional 6% of the energy consumed for their typical interactions with a gateway.

The video footage produced by the surveillance cameras is an important evidence to support criminal investigations. Video evidence can be sourced from public (trusted) as well as private (untrusted) surveillance systems. This raises the issue of establishing integrity and auditability for information provided by the untrusted video sources. In this paper, we focus on a airport ecosystem, where multiple entities with varying levels of trust are involved in producing and exchanging video surveillance information. We present a framework to ensure the data integrity of the stored videos, allowing authorities to validate whether video footage has not been tampered. Our proposal uses a lightweight blockchain technology to store the video metadata as blockchain transactions to support the validation of video integrity. The proposed framework also ensures video auditability and non-repudiation. Our evaluations show that the overhead introduced by employing the blockchain to create and query the transactions introduces a very minor latency of a few milliseconds.

Outbreaks of infectious diseases present a global threat to human health and are considered a major health-care challenge. One major driver for the rapid spatial spread of diseases is human mobility. In particular, the travel patterns of individuals determine their spreading potential to a great extent. These travel behaviors can be captured and modelled using novel location-based data sources, e.g., smart travel cards, social media, etc. Previous studies have shown that individuals who cannot be characterized by their most frequently visited locations spread diseases farther and faster; however, these studies are based on GPS data and mobile call records which have position uncertainty and do not capture explicit contacts. It is unclear if the same conclusions hold for large scale real-world transport networks. In this paper, we investigate how mobility patterns impact disease spread in a large-scale public transit network of empirical data traces. In contrast to previous findings, our results reveal that individuals with mobility patterns characterized by their most frequently visited locations and who typically travel large distances pose the highest spreading risk., 6 Pages, 8 figures and 2 tables. Published in: 2019 IEEE 20th International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM)

Network communications, despite being encrypted, leak crucial information via side channels. WiFi networks are more prone to such side-channel attacks since any attacker within the network’s range can passively eavesdrop the channel. With the increasing number of smart home devices and sensors connecting to private WiFi networks, it is essential to understand the inadvertent information leakage through WiFi side-channels. Our work demonstrates how fine-granular information on the activities happening inside a house can be inferred by passively monitoring WiFi network traffic. In particular, we were able to correctly classify various user interactions with simple IoT devices such as smart bulbs or power sockets as well as advanced voice-based intelligent assistants.

Building operators are required to conduct periodic drills to ensure smooth evacuations in the event of emergencies. However, quantitative evaluation of the drill for adherence to building codes is largely manual and error-prone. Further, unplanned evacuations are seldom documented, let alone evaluated. This paper explores the use of building WiFi data for quantitative evaluations of both planned and unplanned evacuation events. We collect and analyze WiFi connectivity logs spanning a period of 180 days from 14 buildings in a large University campus. For our first contribution, we isolate WiFi data for known planned evacuation drills, conduct floor-level analysis to eliminate noise associated with transient WiFi connections or persistently connected devices, and highlight the anatomy of evacuations across multiple representative buildings each with differing number of levels, exit layouts, and occupant types. Armed with a detailed understanding of the anatomy of a planned evacuation, our second contribution develops a novel method to automatically identify evacuation events from WiFi data; we use it to detect 29 unplanned evacuations, and corroborate them against documented records where available. Our third contribution deduces quantitative measures to compare planned and unplanned evacuations, in terms of evacuation speed and occupancy levels, and further quantifies the man-days of productivity loss arising from unplanned evacuation events across campus. We believe our work is the first to show that building evacuations can be evaluated systematically and accurately at scale using WiFi data, both to corroborate current manual records and to gain new insights.

Next generation mobile networks have proposed the integration of Unmanned Aerial Vehicles (UAVs) as aerial base stations (UAV-BS) to serve ground nodes with potentially varying QoS requirements. However, the dependence on the on-board, limited-capacity battery of the UAV-BS limits their service continuity. While conserving energy is important, meeting the QoS requirements of the ground nodes is equally important. We present an energy-efficient trajectory optimization for the UAV-BS while satisfying QoS requirements. We model the trajectory optimization as an MDP problem and solve it using Q-Learning. Simulation results reveal that our proposed algorithm decreases the average energy consumption by nearly 55% compared to a randomly-served algorithm.

Taking the advantages of reciprocity and randomness of wireless fading channels, key generation via physical layer is attracting more attention. It becomes a remarkable solution for wireless communication in recent years. However, the feasibility under long-range and low data rate scenarios of narrow band low power wide area network (LPWAN) lacks proper studies. In this poster, we introduce a novel modeling method for Long Range Wide Area Network (LoRaWAN) key generation. The approach combines several signal processing techniques and using measured real-time Received Signal Strength Indicator (RSSI) to improve the applicability of key generation as well as increasing key generation rate (KGR) significantly.

Within the Internet of Things (IoT) and blockchain research, there is a growing interest in decentralizing health monitoring systems, to provide improved privacy to patients, without relying on trusted third parties for handling patients&rsquo, sensitive health data. With public blockchain deployments being severely limited in their scalability, and inherently having latency in transaction processing, there is room for researching and developing new techniques to leverage the security features of blockchains within healthcare applications. This paper presents a solution for patients to share their biomedical data with their doctors without their data being handled by trusted third party entities. The solution is built on the Ethereum blockchain as a medium for negotiating and record-keeping, along with Tor for delivering data from patients to doctors. To highlight the applicability of the solution in various health monitoring scenarios, we have considered three use-cases, namely cardiac monitoring, sleep apnoea testing, and EEG following epileptic seizures. Following the discussion about the use cases, the paper outlines a security analysis performed on the proposed solution, based on multiple attack scenarios. Finally, the paper presents and discusses a performance evaluation in terms of data delivery time in comparison to existing centralized and decentralized solutions.

Personal IoT data is a new economic asset that individuals can trade to generate revenue on the emerging data marketplaces. Typically, marketplaces are centralized systems that raise concerns of privacy, single point of failure, little transparency and involve trusted intermediaries to be fair. Furthermore, the battery-operated IoT devices limit the amount of IoT data to be traded in real-time that affects buyer/seller satisfaction and hence, impacting the sustainability and usability of such a marketplace. This work proposes to utilize blockchain technology to realize a trusted and transparent decentralized marketplace for contract compliance for trading IoT data streams generated by battery-operated IoT devices in real-time. The contribution of this paper is two-fold: (1) we propose an autonomous blockchain-based marketplace equipped with essential functionalities such as agreement framework, pricing model and rating mechanism to create an effective marketplace framework without involving a mediator, (2) we propose a mechanism for selection and allocation of buyers' demands on seller's devices under quality and battery constraints. We present a proof-of-concept implementation in Ethereum to demonstrate the feasibility of the framework. We investigated the impact of buyer's demand on the battery drainage of the IoT devices under different scenarios through extensive simulations. Our results show that this approach is viable and benefits the seller and buyer for creating a sustainable marketplace model for trading IoT data in real-time from battery-powered IoT devices., Accepted in SmartComp 2020

The recent outbreak of COVID-19 has taken the world by surprise, forcing lockdowns and straining public health care systems. COVID-19 is known to be a highly infectious virus, and infected individuals do not initially exhibit symptoms, while some remain asymptomatic. Thus, a non-negligible fraction of the population can, at any given time, be a hidden source of transmissions. In response, many governments have shown great interest in smartphone contact tracing apps that help automate the difficult task of tracing all recent contacts of newly identified infected individuals. However, tracing apps have generated much discussion around their key attributes, including system architecture, data management, privacy, security, proximity estimation, and attack vulnerability. In this article, we provide the first comprehensive review of these much-discussed tracing app attributes. We also present an overview of many proposed tracing app examples, some of which have been deployed countrywide, and discuss the concerns users have reported regarding their usage. We close by outlining potential research directions for next-generation app design, which would facilitate improved tracing and security performance, as well as wide adoption by the population at large., Paper has been accepted for publication in IEEE Access. Currently available on IEEE ACCESS early access (see DOI)

Academic certificates have a significant influence on the job market, proving a particular competence or skill of a recipient. However, the ability to verify the authenticity of certificates does not follow its relevance in the labor market, causing several companies to exploit this inefficiency to falsify information or even to make fake certificates. In this context, several proposals based on blockchain appear as a technological alternative to increase the transparency and the ease of verification of these certificates. This chapter discusses the main proposals toward the handling of academic certificates from a technological point of view, discussing the technical aspects that may influence the relationship between confidentiality and transparency as well as application requirements such as performance and reliability in contrast to the blockchain characteristics. Finally, this chapter summarizes the key challenges and opportunities based on this discussion outlining future directions for academic certificate management.

Despite the increasing popularity of commercial usage of UAVs or drone-delivered services, their dependence on the limited-capacity on-board batteries hinders their flight-time and mission continuity. As such, developing in-situ power transfer solutions for topping-up UAV batteries have the potential to extend their mission duration. In this paper, we study a scenario where UAVs are deployed as base stations (UAV-BS) providing wireless Hotspot services to the ground nodes, while harvesting wireless energy from flying energy sources. These energy sources are specialized UAVs (Charger or transmitter UAVs, tUAVs), equipped with wireless power transmitting devices such as RF antennae. tUAVs have the flexibility to adjust their flight path to maximize energy transfer. With the increasing number of UAV-BSs and environmental complexity, it is necessary to develop an intelligent trajectory selection procedure for tUAVs so as to optimize the energy transfer gain. In this paper, we model the trajectory optimization of tUAVs as a Markov Decision Process (MDP) problem and solve it using Q-Learning algorithm. Simulation results confirm that the Q-Learning based optimized trajectory of the tUAVs outperforms two benchmark strategies, namely random path planning and static hovering of the tUAVs.

Human Activity Recognition (HAR) plays an irreplaceable role in various applications such as security, gaming, and assisted living. Recent studies introduce deep learning to mitigate the manual feature extraction (i.e., data representation) efforts and achieve high accuracy. However, there are still challenges in learning accurate representations for sensory data due to the weakness of representation modules and the subject variances. We propose a scheme called Distance-based HAR from Ensembled spatial-temporal Representations (DHARER) to address above challenges. The idea behind DHARER is straightforward—the same activities should have similar representations. We first learn representations of the input sensory segments and latent prototype representations of each class, using a Convolution Neural Network (CNN)-based dual-stream representation module; then the learned representations are projected to activity types by measuring their similarity to the learned prototypes. We have conducted extensive experiments under a strict subject-independent setting on three large-scale datasets to evaluate the proposed scheme, and our experimental results demonstrate superior performance of DHARER to several state-of-the-art methods.

This theme issue will elaborate on the opportunities, challenges, and solutions to be offered by combining blockchain and cyberphysical systems for different application domains. Cyberphysical systems (CPSs) combine physical objects or systems with integrated computing facilities and data storage. Such CPSs can be interconnected in networks, within which they can exchange and share data and information with other objects and systems. The Internet of Things (IoT) in general is making rapid progress by providing connectivity to consumer devices. Blockchain is another promising technology in the information and communications technology domain. Blockchain is a decentralized digital database (ledger), which stores the transactions committed by users. The authenticity of such transactions is verified by the connected community (miners) before adding them to the ledger. Thus, blockchain employs a distributed trust model by eliminating a third -party centralized trust model. In the blockchain, each block bundles an array of transaction records and their cryptographic chain links.

An Intrusion Detection System (IDS) aims to alert users of incoming attacks by deploying a detector that monitors network traffic continuously. As an effort to increase detection capabilities, a set of independent IDS detectors typically work collaboratively to build intelligence of holistic network representation, which is referred to as Collaborative Intrusion Detection System (CIDS). However, developing an effective CIDS, particularly for the IoT ecosystem raises several challenges. Recent trends and advances in blockchain technology, which provides assurance in distributed trust and secure immutable storage, may contribute towards the design of effective CIDS. In this poster abstract, we present our ongoing work on a decentralized CIDS for IoT, which is based on blockchain technology. We propose an architecture that provides accountable trust establishment, which promotes incentives and penalties, and scalable intrusion information storage by exchanging bloom filters. We are currently implementing a proof-of-concept of our modular architecture in a local test-bed and evaluate its effectiveness in detecting common attacks in IoT networks and the associated overhead., Comment: Accepted to ACM/IEEE IoTDI 2020

Person identification technology recognizes individuals by exploiting their unique, measurable physiological and behavioral characteristics. However, the state-of-the-art person identification systems have been shown to be vulnerable, e.g., contact lenses can trick iris recognition and fingerprint films can deceive fingerprint sensors. EEG (Electroencephalography)-based identification, which utilizes the users' brainwave signals for identification and offers a more resilient solution, draw a lot of attention recently. However, the accuracy still requires improvement and very little work is focusing on the robustness and adaptability of the identification system. We propose MindID, an EEG-based biometric identification approach, achieves higher accuracy and better characteristics. At first, the EEG data patterns are analyzed and the results show that the Delta pattern contains the most distinctive information for user identification. Then the decomposed Delta pattern is fed into an attention-based Encoder-Decoder RNNs (Recurrent Neural Networks) structure which assigns various attention weights to different EEG channels based on the importance of channels. The discriminative representations learned from the attention-based RNN are used to recognize the user identification through a boosting classifier. The proposed approach is evaluated over 3 datasets (two local and one public). One local dataset (EID-M) is used for performance assessment and the result illustrates that our model achieves an accuracy of 0.982 which outperforms the baselines and the state-of-the-art. Another local dataset (EID-S) and a public dataset (EEG-S) are utilized to demonstrate the robustness and adaptability, respectively. The results indicate that the proposed approach has the potential to be largely deployed in the practice environment., Published in UbiComp 2018; ACM IMWUT, Vol.2, No. 3, Article No.149, pp 1-23

Recent developments in consumer Unmanned Aerial Vehicles (UAVs) technology have created unprecedented opportunities for their applications in various civil domains. These ubiquitous vehicles of different shapes and sizes with easy and user-friendly configurations are favorite choices for providing different services such as wireless communications, emergency medical deliveries, disaster handling and many more. However, the limited battery life of UAVs pose a challenge to their service continuity, thus mechanisms to extend the UAVs' battery life are required. For service delivery, UAVs consume energy for mechanical functionalities as well as for communicating with other network nodes. To reduce the mechanical energy consumption, the shortest flying path can be considered while selecting a right radio frequency level for UAV's communications can effectively reduce the remaining required energy. In this paper, we analyze energy requirements for providing different communication services using different radio frequency bands. We propose an application-aware, energy-efficient trajectory design method which dynamically adapts the UAV's communication radio frequency to the requested services in the best flying trajectory while considering service level priorities as well. Our simulation results show that our approach can save up to 14% energy while providing even higher Quality of Service (QoS) in a given trajectory.

Over the past few years, activity recognition techniques have attracted unprecedented attentions. Along with the recent prevalence of pervasive e-Health in various applications such as smart homes, automatic activity recognition is being implemented increasingly for rehabilitation systems, chronic disease management, and monitoring the elderly for their personal well-being. In this paper, we present WITS, an end-to-end web-based in-home monitoring system for convenient and efficient care delivery. The system unifies the data- and knowledge-driven techniques to enable a real-time multi-level activity monitoring in a personalized smart home. The core components consist of a novel shared-structure dictionary learning approach combined with rule-based reasoning for continuous daily activity tracking and abnormal activities detection. WITS also exploits an Internet of Things middleware for the scalable and seamless management and learning of the information produced by ambient sensors. We further develop a user-friendly interface, which runs on both iOS and Andriod, as well as in Chrome, for the efficient customization of WITS monitoring services without programming efforts. This paper presents the architectural design of WITS, the core algorithms, along with our solutions to the technical challenges in the system implementation.

Multi-agent systems (MASs) have received tremendous attention from scholars in different disciplines, including computer science and civil engineering, as a means to solve complex problems by subdividing them into smaller tasks. The individual tasks are allocated to autonomous entities, known as agents. Each agent decides on a proper action to solve the task using multiple inputs, e.g., history of actions, interactions with its neighboring agents, and its goal. The MAS has found multiple applications, including modeling complex systems, smart grids, and computer networks. Despite their wide applicability, there are still a number of challenges faced by MAS, including coordination between agents, security, and task allocation. This survey provides a comprehensive discussion of all aspects of MAS, starting from definitions, features, applications, challenges, and communications to evaluation. A classification on MAS applications and challenges is provided along with references for further studies. We expect this paper to serve as an insightful and comprehensive resource on the MAS for researchers and practitioners in the area.

The papers in this special section focus on sensor data computing as a service in the Internet of Things (IoT). The Internet-of-Things (IoT) has gained significant attention over the past decade. It “allows people and things to be Anytime, Anyplace, with Anything and Anyone, ideally using Any path/ network and Any service”. The IoT promises to create a world where everyday objects (also called things) around us are connected to the Internet and communicate with each other with minimum human intervention. The number of things connected to the Internet exceeded the number of people on Earth in 2008. The sensory data these objects produce have significant value to many different parties from supply chain management to healthcare services to many more.

Welcome to the 21st ACM International Conference on Modelling, Analysis and Simulation of Wireless and Mobile Systems (MSWiM), held this year in multicultural Montreal, Canada. Referred as Canada's Cultural Capital, Montreal contains a distinct local culture, being the centre in the nation on the French-language media production. This inspiring city is known for being the source of many talents in fields of visual arts, theatre, music, and dance, hosting several festivals along the year. Being named as a UNESCO City of Design, Montreal's distinguished architecture provides invaluable insight on many aspects, including its history mainly with the Old Port area, Habitat 67, Montreal Biosphere, and the Underground City. Montreal is also a national reference of scientific production and vivid academic life with over five internationally recognized universities.

The performance of low-power wireless sensor networks used to build Internet of Things applications often suffers from radio interference generated by co-located wireless devices or from jammers maliciously placed in their proximity. As IoT devices typically operate in unsupervised large-scale installations, and as radio interference is typically localized and hence affects only a portion of the nodes in the network, it is important to give low-power wireless sensors and actuators the ability to autonomously mitigate the impact of surrounding interference. In this paper we present our approach DynCCA, which dynamically adapts the clear channel assessment threshold of IoT devices to minimize the impact of malicious or unintentional interference on both network reliability and energy efficiency. First, we describe how varying the clear channel assessment threshold at run-time using only information computed locally can help to minimize the impact of unintentional interference from surrounding devices and to escape jamming attacks. We then present the design and implementation of DynCCA on top of ContikiMAC and evaluate its performance on wireless sensor nodes equipped with IEEE 802.15.4 radios. Our experimental investigation shows that the use of DynCCA in dense IoT networks can increase the packet reception rate by up to 50% and reduce the energy consumption by a factor of 4.

Wireless Sensor Networks (WSN) are part of the technical fundament enabling the Internet of Things (IoT), where sensing and actuator nodes instantaneously interact with the environment at large. As such they become part of everyday life and drive applications as diverse as medical monitoring, smart homes, smart environment, and smart factories, to name but a few. To acquire data, individual sensors interact with the physical environment by sensing physical phenomena in proximity. The wireless network connectivity is leveraged to collect the raw data or pre-processed events, and to disseminate code, queries or commands. Actuating capabilities facilitate instant interactions with the environment or application processes. Experience on how to operate large scale heterogeneous WSNs in (critical) real-world applications is still scarce, and operational considerations are often an afterthought to WSN deployment. A principled look into the metrics, i.e., a standard or best practice of measurement of the vital parameters in WSNs is still missing. In this article, we contribute a survey on the most important metrics to characterize the performance of WSNs. We define an abstract system model for WSNs, take a look on what the WSN community considers metrics that matter, and categorize the metrics into scopes of relevance. We discuss the properties of the metrics as well as practical aspects on how to obtain and process them. Our survey can serve as a manual for implementors and operators of WSNs in the IoT.

Proper incentive mechanisms are critical for mobile crowdsensing systems to motivate people to actively and persistently participate. This article provides an exposition of design principles of six incentive mechanisms, drawing special attention to the sustainability issue. We cover three primary classes of incentive mechanisms: auctions, lotteries, and trust and reputation systems, as well as three other frameworks of promising potential: bargaining games, contract theory, and market-driven mechanisms., Comment: 6 pages, 4 figures, 2 tables

Advances in the manufacturing of miniaturized low-power embedded systems are paving the way for ultra-lightweight wearable cameras that can visually log the minute details of people's daily lives. This survey highlights the possible applications of wearable cameras, from industrial to personal, and outlines the main challenges in realizing their full potential.

The Internet of Things (IoT) is transforming our physical world into a complex and dynamic system of connected devices on an unprecedented scale. Connecting everyday physical objects is creating new business models, improving processes and reducing costs and risks. Recently, blockchain technology has received a lot of attention from the community as a possible solution to overcome security issues in IoT. However, traditional blockchains (such as the ones used in Bitcoin and Ethereum) are not well suited to the resource-constrained nature of IoT devices and also with the large volume of information that is expected to be generated from typical IoT deployments. To overcome these issues, several researchers have presented lightweight instances of blockchains tailored for IoT. For example, proposing novel data structures based on blocks with decoupled and appendable data. However, these researchers did not discuss how the consensus algorithm would impact their solutions, i.e., the decision of which consensus algorithm would be better suited was left as an open issue. In this paper, we improved an appendable-block blockchain framework to support different consensus algorithms through a modular design. We evaluated the performance of this improved version in different emulated scenarios and studied the impact of varying the number of devices and transactions and employing different consensus algorithms. Even adopting different consensus algorithms, results indicate that the latency to append a new block is less than 161ms (in the more demanding scenario) and the delay for processing a new transaction is less than 7ms, suggesting that our improved version of the appendable-block blockchain is efficient and scalable, and thus well suited for IoT scenarios.

© 2019 Copyright held by the owner/author(s). Publication rights licensed to ACM. Online ride-sharing platforms have become a critical part of the urban transportation system. Accurately recommending hotspots to drivers in such platforms is essential to help drivers find passengers and improve users' experience, which calls for efficient passenger demand prediction strategy. However, predicting multi-step passenger demand is challenging due to its high dynamicity, complex dependencies along spatial and temporal dimensions, and sensitivity to external factors (meteorological data and time meta). We propose an end-to-end deep learning framework to address the above problems. Our model comprises three components in pipeline: 1) a cascade graph convolutional recurrent neural network to accurately extract the spatial-temporal correlations within citywide historical passenger demand data; 2) two multi-layer LSTM networks to represent the external meteorological data and time meta, respectively; 3) an encoder-decoder module to fuse the above two parts and decode the representation to predict over multi-steps into the future. The experimental results on three real-world datasets demonstrate that our model can achieve accurate prediction and outperform the most discriminative state-of-the-art methods.

The exponential increase in connected devices with built-in sensing, processing, and communication capabilities has fuelled the development of IoT applications, which creates new ecosystems for device-to-device interactions, supports smart environments, and leads to new business models. Empowered by these capabilities, IoT devices interact with each other and their environments to collect, process, and share data. Security, privacy, and reliability of data are major concerns that need to be addressed for the development of IoT applications. Recently, blockchain technology has attracted significant interest from researchers and industry leaders due to its potential for enhancing security, privacy, and reliability of the data. Blockchain offers distributed and immutable ledgers for IoT communications in the form of tamper-proof records, built-in cryptocurrency support for transactions between devices and other entities, and smart contracts to execute automated programs when certain conditions are met. Although there are potential benefits of the integration of blockchain technology to IoT, the integration introduces new challenges, such as scalability, in the design of blockchains suited for IoT applications. In this chapter, we explore key benefits and design challenges for blockchain technologies, and potential applications of blockchain technologies for IoT.