IoT devices have significantly altered the methods of interaction, operation, and functionality within home environments. However, individuals, particularly those with limited technical proficiency who stand to gain the most from these advancements, likely encounter challenges stemming from the intricate setup processes, a critical stage with the potential to limit their widespread adoption. Thus, we focus on the user experience during the setup phase of mainstream smart home devices and conduct an empirical study of 15 representative smart home IoT devices. We scrupulously examine their setup processes, as well as accompanying instructions and user manuals, to assess multi-faceted usability concerns. Our findings reveal 19 usability issues, indicating notable barriers, inconsistencies, and a lack of intuitiveness, which may deter consumers from successfully configuring and using these devices.

IoT devices are susceptible to botnet malware due to inherent system limitations. Once compromised, these devices can be exploited to leak private user information or launch DDoS attacks. In this paper, we present Wital, an adaptable mitigation strategy that curtails the malicious use of IoT devices through a stringent whitelist-based approach. This method combines static manufacturer usage profiling and dynamic traffic monitoring into a client-side firewall. Our strategy ensures that, even if devices are compromised, their impact is minimized. Using the P4 language for programmable data planes, we prototype the firewall solutions and demonstrate that this solution can significantly reduce potential device exploitation.

IoT devices benefit from nuanced policy enforcement to ensure safe, reliable, and efficient operations. However, the practical implementation of IoT policy enforcement systems involves multifaceted complexities. This paper delves into three-dimensional challenges: the intricacies of architecture and interoperational support, the expressiveness and safety concerns of policy languages, and the steep learning curve associated with policy creation. We investigate these areas, evaluate options, and prototype systems, considering the WoT, Pkl, and LLMs as potential solutions to address each challenge, respectively.

We propose and implement Directory-Based Access Control (DBAC), a flexible and systematic access control approach for geographically distributed multi-administration IoT systems. DBAC designs and relies on a particular module, IoT directory, to store device metadata, manage federated identities, and assist with cross-domain authorization. The directory service decouples IoT access into two phases: discover device information from directories and operate devices through discovered interfaces. DBAC extends attribute-based authorization and retrieves diverse attributes of users, devices, and environments from multi-faceted sources via standard methods, while user privacy is protected. To support resource-constrained devices, DBAC assigns a capability token to each authorized user, and devices only validate tokens to process a request.

To scale the Internet of Things (IoT) beyond a single home or enterprise, we need an effective mechanism to manage the growth of data, facilitate resource discovery and name resolution, encourage data sharing, and foster cross-domain services. To address these needs, we propose a GlObaL Directory for Internet of Everything (GOLDIE). GOLDIE is a hierarchical location-based IoT directory architecture featuring diverse user-oriented modules and federated identity management. IoT-specific features include discoverability, aggregation and geospatial queries, and support for global access. We implement and evaluate the prototype on a Raspberry Pi and Intel mini servers. We show that a global implementation of GOLDIE could decrease service access latency by 87% compared to a centralized-server solution.

As IoT services scale up from single homes to smart cities, directories and mapping services are needed to manage potentially millions of devices. However, directory service providers will likely struggle to accommodate the increasing number of IoT devices, made more challenging by their heterogeneous metadata and the large volume of queries. One of the critical challenges, the high heterogeneity of IoT, is being addressed by a working standard of W3C, which formalizes a physical or virtual device as a formatted Thing Description (TD).We propose a local directory service architecture with a series of design requirements. With a focus on query performance, we build a proof-of-concept system to store metadata of IoT devices as TDs in terms of the working standard. A Raspberry Pi is configured to investigate the query performance of relational database and non-relational database as the classic choices for internal directories. Evaluation results demonstrate that compared with relational database, non-relational database can achieve 2.9 times higher resilience on property query and 2.35 times faster processing on spatial query, with mild loss on aggregation query.

Multi-channel data broadcast attracts increasingly focus in recent years. For the complex and large multimedia data like images, audios, and videos etc, multi-channel data broadcast is a promising approach to mitigate various limitations of data dissemination in mobile environment, such as narrow bandwidth, unreliable connections, and battery limitation. However, existing data broadcast schemes are inefficient for employing multiple channels. In this paper, we present five novel multimedia data broadcast schemes (SDAA, ISDAA, LDAA, AEA, and COA) specifically designed for wireless multichannel communications. The key idea behind those schemes is to utilize SVC (Scalable Video Coding) to generate data segments with different qualities and implement indexing and channel assignment to minimize the expected waiting time for clients. In terms of waiting time minimization, we prove theoretically that SDAA is a 4/3-approximation algorithm and ISDAA is a 6/5-approximation algorithm for global allocation, and AEA can achieve locally optimal solution. We show that integrating SDAA and AEA form a best schedule for practical applications. We provide numerical experiments by using a SVC dataset and a YouTube dataset to evaluate the system performance and prove the efficiency of our schemes. We also propose a multiple video broadcast framework and analyze its performance.

Aggressively provisioned data centers achieve great cost savings by over-committing the very expensive power distribution infrastructure. However, existing proposals for managing load power demand in such a data center are largely utilization-driven, overlooking power-related interferences among users. An important observation is that some tasks can impact existing power budget management framework and disrupt normal operation by taking away the precious public power capacity. This vulnerability exposes data centers to a new type of risk that we call power grab, which is essentially hostile power resource competition. It could worsen the performance-utilization tradeoff in a power-constrained computing environment. Anticipating a growing case for power-oriented com-petition, we propose CFP, a resilient power capacity management frame-work for improving the fairness and service quality in scale-out data centers. Our solution features a market-based power re-source allocation and billing scheme that involves users in the loop. It allows the data center to bypass the formidable task of identifying malicious users and defend against power grab with reward and punishment incentives. We build a proof-of-concept system and also evaluate our design with realistic Google cluster traces. Compared to prior arts, CFP can increase the average performance-cost ratio by 1.8X. It can boost the total throughput in an APDC by 15% under severe power contention. Our design allows scale-out data centers to safely exploit the benefits that power over-subscription may provide, with minor overhead.

As the growing demand for the large-scale and diverse services, Device-to-Device (D2D) communications have been considered as one of the most promising methods in 5G to improve the capacity of the service network and the quality of experience (QoE) for mobile users, especially those located in specific areas, asking for the same kind of multimedia services. However, it is still tough for service providers to manage these devices in a short time because different users have different QoE requirements and the arrival and departure of each user are random, which severely affect the continuity of service for other neighbor users. In this paper, we propose a fast D2D Multimedia Transmission Scheme (named DMaster) based on local search to overcome these drawbacks and solve the D2D service initialization problem, which is NP-Complete. DMaster organizes D2D communications as a spanning tree transmitting SVC-based Adaptive Streaming (SVC-AS). Furthermore, we prove that the running time of DMaster utilizing disjoint-set is O(|L||D|α(|L|, |D|)), where L is the set of potential D2D links, D is the set of devices, and α is the inverse Ackerman function. Extensive simulations show that the proposed scheme guarantees QoE, fairness, and continuity of service and supports a large number of users. To the best of our knowledge, it is the first work focusing on the SVC-based fast service scheme in D2D communications.

Crowdsourcing has been considered as one of the most promising services in recent years. More and more crowdsourcing platforms allocate tasks over the social network due to its pervasiveness. Although most research focuses on direct contribution-based task allocation with some budget constraints, a robust task allocation scheme should also consider the task allocation in the word-of-mouth (WoM) mode, in which tasks are delivered from workers to workers. In this paper, we discuss an NP-Complete problem, cost-effective and budget-balanced task allocation (CBTA) problem, specially for the WoM mode crowdsourcing over social network, which aims to minimize the overall budget consumption as well as balance the budgets among target social groups. Furthermore, we propose two heuristic algorithms CB-greedy and CB-local based on greedy strategy and local search technique respectively to construct a spanning tree for task allocation. We prove that the running time of CB-greedy is O(m2log m) while CB-local utilizing disjoint-set achieves O(mna(m, n)), where m is the number of edges indicating interactions of social groups, n is the number of social groups, and α is the inverse Ackerman function. Extensive simulations show that the proposed algorithms guarantee the criteria to a large extent. To the best of our knowledge, it is the first work jointly optimizing cost effectiveness and budget balance in the WoM mode crowdsourcing systems.