Internet of things (IoT) produces massive data from devices embedded with sensors. The IoT data allows creating profitable services using machine learning. However, previous research does not address the problem of optimal pricing and bundling of machine learning-based IoT services. In this paper, we define the data value and service quality from a machine learning perspective. We present an IoT market model which consists of data vendors selling data to service providers, and service providers offering IoT services to customers. Then, we introduce optimal pricing schemes for the standalone and bundled selling of IoT services. In standalone service sales, the service provider optimizes the size of bought data and service subscription fee to maximize its profit. For service bundles, the subscription fee and data sizes of the grouped IoT services are optimized to maximize the total profit of cooperative service providers. We show that bundling IoT services maximizes the profit of service providers compared to the standalone selling. For profit sharing of bundled services, we apply the concepts of core and Shapley solutions from cooperative game theory as efficient and fair allocations of payoffs among the cooperative service providers in the bundling coalition.

We propose a new framework combining dynamic sampling rates for healthcare sensors driven by user behavior, and an adaptive MAC scheduling scheme applied to the time-slotted channel hopping (TSCH) protocol in IEEE 802.15.4, which provides high throughput and reliable communications. First, we introduce a system software architecture for machine-learning-assisted healthcare monitoring that detects the user's behavior using edge computing and adjusts the sampling rates of the healthcare sensors accordingly. Second, we propose an adaptive MAC scheduling scheme for TSCH based on a state machine model that reacts to the dynamic traffic generated by the healthcare sensors. In case of an urgent state, the MAC scheduler automatically allocates extra timeslots to the appropriate sensors so as to enable the reliable transfer of high-resolution sensor data for further analysis. Experimental results from our testbed, implemented in Contiki-OS on the OpenMote-CC2538 platform, show that the proposed adaptive scheduling scheme can respond quickly to changes in user behavior and ensure the reliable transfer of sensor data in emergency situations.

Mobile crowd sensing is an emerging sensing paradigm where sensing applications buy sensor data from mobile smartphone users (workers) instead of deploying their own sensor networks to estimate some statistics of a spatial event. In many spatial monitoring applications, the crowdsourcer needs to incentivize smartphone users to contribute sensing data so that the collected dataset has good spatial coverage. To further incentivize privacy-concerned workers to contribute, we propose a Stackelberg incentive framework that allows workers to specify their location privacy requirements while also increasing the spatial coverage of the collected dataset. We then derive a unique Stackelberg equilibrium which demonstrates the stability of our approach. Our simulation results show that our approach is significantly better in terms of data utility than the non-location-aware and uniform-reward approaches.

With the emerging sensing technologies, such as mobile crowdsensing and Internet of Things, people-centric data can be efficiently collected and used for analytics and optimization purposes. These data are typically required to develop and render people-centric services. In this paper, we address the privacy implication, optimal pricing, and bundling of people-centric services. We first define the inverse correlation between the service quality and privacy level from data analytics perspectives. We then present the profit maximization models of selling standalone, complementary, and substitute services. Specifically, the closed-form solutions of the optimal privacy level and subscription fee are derived to maximize the gross profit of service providers. For interrelated people-centric services, we show that cooperation by service bundling of complementary services is profitable compared with the separate sales but detrimental for substitutes. We also show that the market value of a service bundle is correlated with the degree of contingency between the interrelated services. Finally, we incorporate the profit sharing models from game theory for dividing the bundling profit among the cooperative service providers.

Privacy-preserving routing protocols in wireless networks frequently utilize additional artificial traffic to hide the identities of communicating source-destination pairs. Usually, the addition of artificial traffic is done heuristically with no guarantees that the transmission cost, latency, and so on, are optimized in every network topology. In this paper, we explicitly examine the privacy-utility tradeoff problem for wireless networks and develop a novel privacy-preserving routing algorithm called optimal privacy enhancing routing algorithm (OPERA). OPERA uses a statistical decision-making framework to optimize the privacy of the routing protocol given a utility (or cost) constraint. We consider global adversaries with both lossless and lossy observations that use the Bayesian maximum-a-posteriori (MAP) estimation strategy. We formulate the privacy-utility tradeoff problem as a linear program, which can be efficiently solved. Our simulation results demonstrate that OPERA reduces the adversary's detection probability by up to 50% compared to the random Uniform and Greedy heuristics, and up to five times compared to a baseline scheme. In addition, OPERA also outperforms the conventional information-theoretic mutual information approach.

Time synchronization is an enabling service that allows devices to share a consistent notion of time and thus makes it easier to build efficient and robust collaborative services. However, existing synchronization protocols based on wireless packet transmissions are not energy efficient because powering the radio often consumes a significant fraction of the energy budget. In this paper, we propose PSync, a visible light-based time synchronization protocol that relies on an LED light source and is highly energy efficient for the receivers. The key novelty in our protocol is the use of a De Bruijn sequence to provide a rough estimate of time using a minimum amount of information. Experiments show that our scheme achieves an average synchronization error of 1.3 timer ticks (32 μs per clock tick). In addition, the additional energy consumed for one round of synchronization based on PSync can be as low as 19% of the energy needed to receive a small packet (1 byte) using IEEE 802.15.4 radio.

We develop a new location spoofing detection algorithm for geo-spatial tagging and location-based services in the Internet of Things (IoT), called enhanced location spoofing detection using audibility (ELSA), which can be implemented at the backend server without modifying existing legacy IoT systems. ELSA is based on a statistical decision theory framework and uses two-way time-of-arrival (TW-TOA) information between the user's device and the anchors. In addition to the TW-TOA information, ELSA exploits the implicit audibility information (or outage information) to improve detection rates of location spoofing attacks. Given TW-TOA and audibility information, we derive the decision rule for the verification of the device's location, based on the generalized likelihood ratio test. We develop a practical threat model for delay measurements' spoofing scenarios, and investigate in detail the performance of ELSA in terms of detection and false alarm rates. Our extensive simulation results on both synthetic and real-world datasets demonstrate the superior performance of ELSA compared to conventional non-audibility-aware approaches.

We present a phantom-receiver-based routing scheme to enhance the anonymity of each source-destination pair (or contextual privacy) while using an adjustable amount of overhead. We also study how traditional network coding and opportunistic routing can leak contextual privacy. We then incorporated both network coding and opportunistic routing into our scheme for better network performance and show how we mitigate its vulnerability. Contrary to prior works, we allow the destination to anonymously submit an acknowledgment to the source for enhanced reliability. Performance analysis and simulations are used to demonstrate the efficacy of the proposed scheme against commonly considered traffic analysis attacks.

We study two variations of the distributed storage allocation problem. The goal is to allocate a given storage budget in a distributed storage system for maximum reliability. It was recently discovered that this problem is related to an old conjecture in extremal combinatorics, on the maximum number of edges in a hypergraph subject to a constraint on its maximum matching number. The conjecture was recently verified in some regimes. In this paper we assume that the conjecture is true and establish new results for the optimal allocation for a variety of parameter values. We also derive new performance bounds that are independent of the conjecture, and compare them to the best previously known bounds.

We consider a real-time streaming system where messages created at regular time intervals at a source are encoded for transmission to a receiver over a packet erasure link; the receiver must subsequently decode each message within a given delay from its creation time. We study a bursty erasure model in which all erasure patterns containing erasure bursts of a limited length are admissible. For certain classes of parameter values, we provide code constructions that asymptotically achieve the maximum message size among all codes that allow decoding under all admissible erasure patterns. We also study an i.i.d. erasure model in which each transmitted packet is erased independently with the same probability; the objective is to maximize the decoding probability for a given message size. We derive an upper bound on the decoding probability for any time-invariant code, and show that the gap between this bound and the performance of a family of time-invariant intrasession codes is small in the high reliability regime.

We consider a real-time streaming system where messages are created sequentially at the source, and are encoded for transmission over a packet erasure channel. Each message must subsequently be decoded at the receiver within a given delay from its creation time. We consider code design and maximum message rates when all messages must be decodable by their respective deadlines under a specified set of erasure patterns (erasure model). Specifically, we provide a code construction that achieves the optimal rate for an asymptotic number of messages, under erasure models containing a limited number of erasures per coding window, per sliding window, and containing erasure bursts of a limited length.

We examine the problem of allocating a given total storage budget in a distributed storage system for maximum reliability. A source has a single data object that is to be coded and stored over a set of storage nodes; it is allowed to store any amount of coded data in each node, as long as the total amount of storage used does not exceed the given budget. A data collector subsequently attempts to recover the original data object by accessing only the data stored in a random subset of the nodes. By using an appropriate code, successful recovery can be achieved whenever the total amount of data accessed is at least the size of the original data object. The goal is to find an optimal storage allocation that maximizes the probability of successful recovery. This optimization problem is challenging in general because of its combinatorial nature, despite its simple formulation. We study several variations of the problem, assuming different allocation models and access models. The optimal allocation and the optimal symmetric allocation (in which all nonempty nodes store the same amount of data) are determined for a variety of cases. Our results indicate that the optimal allocations often have nonintuitive structure and are difficult to specify. We also show that depending on the circumstances, coding may or may not be beneficial for reliable storage.

We examine the problem of creating an encoded distributed storage representation of a data object for a network of mobile storage nodes so as to achieve the optimal recovery delay. A source node creates a single data object and disseminates an encoded representation of it to other nodes for storage, subject to a given total storage budget. A data collector node subsequently attempts to recover the original data object by contacting other nodes and accessing the data stored in them. By using an appropriate code, successful recovery is achieved when the total amount of data accessed is at least the size of the original data object. The goal is to find an allocation of the given budget over the nodes that optimizes the recovery delay incurred by the data collector; two objectives are considered: (i) maximization of the probability of successful recovery by a given deadline, and (ii) minimization of the expected recovery delay. We solve the problem completely for the second objective in the case of symmetric allocations (in which all nonempty nodes store the same amount of data), and show that the optimal symmetric allocation for the two objectives can be quite different. A simple data dissemination and storage protocol for a mobile delay-tolerant network is evaluated under various scenarios via simulations. Our results show that the choice of storage allocation can have a significant impact on the recovery delay performance, and that coding may or may not be beneficial depending on the circumstances.

We consider the problem of optimally allocating a given total storage budget in a distributed storage system. A source has a data object which it can code and store over a set of storage nodes; it is allowed to store any amount of coded data in each node, as long as the total amount of storage used does not exceed the given budget. A data collector subsequently attempts to recover the original data object by accessing each of the nodes independently with some constant probability. By using an appropriate code, successful recovery occurs when the total amount of data in the accessed nodes is at least the size of the original data object. The goal is to find an optimal storage allocation that maximizes the probability of successful recovery. This optimization problem is challenging because of its discrete nature and nonconvexity, despite its simple formulation. Symmetric allocations (in which all nonempty nodes store the same amount of data), though intuitive, may be suboptimal; the problem is nontrivial even if we optimize over only symmetric allocations. Our main result shows that the symmetric allocation that spreads the budget maximally over all nodes is asymptotically optimal in a regime of interest. Specifically, we derive an upper bound for the suboptimality of this allocation and show that the performance gap vanishes asymptotically in the specified regime. Further, we explicitly find the optimal symmetric allocation for a variety of cases. Our results can be applied to distributed storage systems and other problems dealing with reliability under uncertainty, including delay tolerant networks (DTNs) and content delivery networks (CDNs).

We consider the problem of optimally allocating a given total storage budget in a distributed storage system. A source has a data object which it can code and store over a set of storage nodes; it is allowed to store any amount of data in each storage node, subject to a given total storage budget constraint. A data collector subsequently attempts to recover the original data object by accessing a random fixed-size subset of these storage nodes. Successful recovery of the data object occurs when the total amount of coded data in this subset of storage nodes is at least the size of the original data object. The goal is to determine the amount of data to store in each storage node so that the probability of successful recovery is maximized. We solve this problem in the high recovery probability regime. Our results can be applied to a variety of distributed storage systems, including delay tolerant networks (DTNs), content delivery networks (CDNs), and sensor networks.