Context-awareness for mobile sensing: a survey and future directions

The evolution of smartphones together with increasing computational power have empowered developers to create innovative context-aware applications for recognizing user related social and cognitive activities in any situation and at any location. The existence and awareness of the context provides the capability of being conscious of physical environments or situations around mobile device users. This allows network services to respond proactively and intelligently based on such awareness. The key idea behind context-aware applications is to encourage users to collect, analyze and share local sensory knowledge in the purpose for a large scale community use by creating a smart network. The desired network is capable of making autonomous logical decisions to actuate environmental objects, and also assist individuals. However, many open challenges remain, which are mostly arisen due to the middleware services provided in mobile devices have limited resources in terms of power, memory and bandwidth. Thus, it becomes critically important to study how the drawbacks can be elaborated and resolved, and at the same time better understand the opportunities for the research community to contribute to the context-awareness. To this end, this paper surveys the literature over the period of 1991-2014 from the emerging concepts to applications of context-awareness in mobile platforms by providing up-to-date research and future research directions. Moreover, it points out the challenges faced in this regard and enlighten them by proposing possible solutions

Privacy-Constrained Location Accuracy in CooperativeWearable Networks in Multi-Floor Buildings

This paper proposes a geometric dilution-of-precision approach to quantize the privacy-aware location errors in a cooperative wearable network with opportunistic positioning. The main hypothesis is that, a wearable inside a multi-floor building could localize itself based on cooperative pseudoranges measurements from nearby wearables, as long as the nearby wearables are heard above the sensitivity limit and as long as nearby wearables choose to disclose their own positions. A certain percentage of wearables, denoted by γ, is assumed to not want to disclose their positions in order to preserve their privacy. Our paper investigates the accuracy limits under the privacy constraints with variable γ and according to various building maps and received signal strength measurements extracted from real buildings. The data (wearable positions and corresponding power maps) are synthetically generated using a floor-and-wall path-loss model with statistical parameters extracted from real-field measurements. It is found that the network is tolerant to about 30% of the wearables not disclosing their position (i.e., opting for a full location-privacy mode).Peer reviewe

Opportunistic and Context-aware Affect Sensing on Smartphones: The Concept, Challenges and Opportunities

Opportunistic affect sensing offers unprecedented potential for capturing spontaneous affect ubiquitously, obviating biases inherent in the laboratory setting. Facial expression and voice are two major affective displays, however most affect sensing systems on smartphone avoid them due to extensive power requirement. Encouragingly, due to the recent advent of low-power DSP (Digital Signal Processing) co-processor and GPU (Graphics Processing Unit) technology, audio and video sensing are becoming more feasible. To properly evaluate opportunistically captured facial expression and voice, contextual information about the dynamic audio-visual stimuli needs to be inferred. This paper discusses recent advances of affect sensing on the smartphone and identifies the key barriers and potential solutions of implementing opportunistic and context-aware affect sensing on smartphone platforms

Optimal Witnessing of Healthcare IoT Data Using Blockchain Logging Contract

Verification of data generated by wearable sensors is increasingly becoming of concern to health service providers and insurance companies. There is a need for a verification framework that various authorities can request a verification service for the local network data of a target IoT device. In this paper, we leverage blockchain as a distributed platform to realize an on-demand verification scheme. This allows authorities to automatically transact with connected devices for witnessing services. A public request is made for witness statements on the data of a target IoT that is transmitted on its local network, and subsequently, devices (in close vicinity of the target IoT) offer witnessing service. Our contributions are threefold: (1) We develop a system architecture based on blockchain and smart contract that enables authorities to dynamically avail a verification service for data of a subject device from a distributed set of witnesses which are willing to provide (in a privacy-preserving manner) their local wireless measurement in exchange of monetary return; (2) We then develop a method to optimally select witnesses in such a way that the verification error is minimized subject to monetary cost constraints; (3) Lastly, we evaluate the efficacy of our scheme using real Wi-Fi session traces collected from a five-storeyed building with more than thirty access points, representative of a hospital. According to the current pricing schedule of the Ethereum public blockchain, our scheme enables healthcare authorities to verify data transmitted from a typical wearable device with the verification error of the order 0.01% at cost of less than two dollars for one-hour witnessing service.Comment: 12 pages, 12 figure

Anticipatory Mobile Computing: A Survey of the State of the Art and Research Challenges

Today's mobile phones are far from mere communication devices they were ten years ago. Equipped with sophisticated sensors and advanced computing hardware, phones can be used to infer users' location, activity, social setting and more. As devices become increasingly intelligent, their capabilities evolve beyond inferring context to predicting it, and then reasoning and acting upon the predicted context. This article provides an overview of the current state of the art in mobile sensing and context prediction paving the way for full-fledged anticipatory mobile computing. We present a survey of phenomena that mobile phones can infer and predict, and offer a description of machine learning techniques used for such predictions. We then discuss proactive decision making and decision delivery via the user-device feedback loop. Finally, we discuss the challenges and opportunities of anticipatory mobile computing.Comment: 29 pages, 5 figure

Evaluating How Smartphone Contact Tracing Technology Can Reduce the Spread of Infectious Diseases: The Case of COVID-19

[EN] Detecting and controlling the diffusion of infectious diseases such as COVID-19 is crucial to managing epidemics. One common measure taken to contain or reduce diffusion is to detect infected individuals and trace their prior contacts so as to then selectively isolate any individuals likely to have been infected. These prior contacts can be traced using mobile devices such as smartphones or smartwatches, which can continuously collect the location and contacts of their owners by using their embedded localisation and communications technologies, such as GPS, Cellular networks, Wi-Fi, and Bluetooth. This paper evaluates the effectiveness of these technologies and determines the impact of contact tracing precision on the spread and control of infectious diseases. To this end, we have created an epidemic model that we used to evaluate the efficiency and cost (number of people quarantined) of the measures to be taken, depending on the smartphone contact tracing technologies used. Our results show that in order to be effective for the COVID-19 disease, the contact tracing technology must be precise, contacts must be traced quickly, and a significant percentage of the population must use the smartphone contact tracing application. These strict requirements make smartphone-based contact tracing rather ineffective at containing the spread of the infection during the first outbreak of the virus. However, considering a second wave, where a portion of the population will have gained immunity, or in combination with some other more lenient measures, smartphone-based contact tracing could be extremely useful.This work was supported in part by the Ministerio de Ciencia, Innovacion y Universidades, Spain, under Grant RTI2018-096384-B-I00.Hernández-Orallo, E.; Manzoni, P.; Tavares De Araujo Cesariny Calafate, CM.; Cano, J. (2020). Evaluating How Smartphone Contact Tracing Technology Can Reduce the Spread of Infectious Diseases: The Case of COVID-19. IEEE Access. 8:99083-99097. https://doi.org/10.1109/ACCESS.2020.2998042S9908399097