Trigger-Action Program (TAP) is a simple but powerful format to realize intelligent IoT applications, especially in home automation scenarios. Existing trace-driven approaches and in-situ programming approaches depend on either customized interaction commands or well-labeled datasets, resulting in limited applicable scenarios. In this paper, we propose ChatIoT, a zero-code TAP generation system based on large language models (LLMs). With a novel context-aware compressive prompting scheme, ChatIoT is able to automatically generate TAPs from user requests in a token-efficient manner and deploy them to the TAP runtime. Further, for those TAP requests including unknown sensing abilities, ChatIoT can also generate new AI models with knowledge distillation by multimodal LLMs, with a novel model customization method based on deep reinforcement learning. We implemented ChatIoT and evaluated its performance extensively. Results show that ChatIoT can reduce token consumption by 26.1-84.9% and improve TAP generation accuracy by 4.2-65.5% compared to state-of-the-art approaches in multiple settings. We also conducted a real user study, and ChatIoT can achieve 91.57% TAP generation accuracy.

Multihop low-power wireless communication is a key feature of Wireless Sensor Networks (WSNs). Although with a lot of efforts, the performance of multihop WSNs in practical deployment could still be unpredictable and unsatisfactory, such as high packet transmission latency and short network lifetime. Narrowband Internet of Things (NB-IoT) is an emerging cellular technology for providing wide-area coverage. In this paper, we investigate the possibilities of deploying 802.15.4 and NB-IoT equipped sink nodes (i.e., gateways) into existing WSNs to achieve the predictable and improved performance in terms of end-to-end reliability, end-to-end latency, and network lifetime. To calculate these key network-wide metrics, we propose a hybrid analytical model for 802.15.4 and NB-IoT traffic, taking the new features of NB-IoT into account. Based on the established model, we formally consider two types of fundamental problems, i.e., the minimum gateway deployment and the optimal κ-gateway deployment. We implement our framework and extensively evaluate it in realistic as well as synthetic WSNs. Results demonstrate that our approach can provide significant performance gains, e.g., the network lifetime can be extended by 37.4%-80.3% by deploying only one sink node. Compared with a state-of-the-art method, our approach improves the network lifetime by 12.6%-31.8%.

Data dissemination is a fundamental service offered by low-power wireless networks. Sender selection is the key to the dissemination performance and has been extensively studied. Sender impact metric plays a significant role in sender selection, since it determines which senders are selected for transmission. Recent studies have shown that spatial link diversity has a significant impact on the efficiency of broadcast. However, the existing metrics overlook such impact. Besides, they consider only gains but ignore the costs of sender candidates. As a result, existing works cannot achieve accurate estimation of the sender impact. Moreover, they cannot well support data dissemination with network coding, which is commonly used for lossy environments. In this paper, we first propose a novel sender impact metric, namely, γ, which jointly exploits link quality and spatial link diversity to calculate the gain/cost ratio of the sender candidates. Then, we develop a generic sender selection scheme based on the γ metric (called γ-component) that can generally support both types of dissemination using native packets and network coding. Extensive evaluations are conducted through real testbed experiments and large-scale simulations. The performance results and analysis show that γ achieves far more accurate impact estimation than the existing works. In addition, the dissemination protocols based on γ-component outperform the existing protocols in terms of completion time and transmissions (by 20.5% and 23.1%, respectively).

Mobile advertising plays a vital role in the mobile app ecosystem. A major threat to the sustainability of this ecosystem is click fraud , i.e., ad clicks performed by malicious code or automatic bot problems. Existing click fraud detection approaches focus on analyzing the ad requests at the server side. However, such approaches may suffer from high false negatives since the detection can be easily circumvented, e.g., when the clicks are behind proxies or globally distributed. In this paper, we present AdSherlock, an efficient and deployable click fraud detection approach at the client side (inside the application) for mobile apps. AdSherlock splits the computation-intensive operations of click request identification into an offline procedure and an online procedure. In the offline procedure, AdSherlock generates both exact patterns and probabilistic patterns based on URL (Uniform Resource Locator) tokenization. These patterns are used in the online procedure for click request identification and further used for click fraud detection together with an ad request tree model. We implement a prototype of AdSherlock and evaluate its performance using real apps. The online detector is injected into the app executable archive through binary instrumentation. Results show that AdSherlock achieves higher click fraud detection accuracy compared with state of the art, with negligible runtime overhead.

The ability to automatically locate sensor nodes is essential in many Wireless Sensor Network (WSN) applications. To reduce the number of beacons, many mobile-assisted approaches have been proposed. Current mobile-assisted approaches for localization require special hardware or belong to centralized localization algorithms involving some deterministic approaches due to the fact that they explicitly consider the impreciseness of location estimates. In this paper, we first propose a range-free, distributed and probabilistic Mobile Beacon-assisted Localization (MBL) approach for static WSNs. Then, we propose another approach based on MBL, called Adapting MBL (A-MBL), to increase the efficiency and accuracy of MBL by adapting the size of sample sets and the parameter of the dynamic model during the estimation process. Evaluation results show that the accuracy of MBL and A-MBL outperform both Mobile and Static sensor network Localization (MSL) and Arrival and Departure Overlap (ADO) when both of them use only a single mobile beacon for localization in static WSNs.