Conventional performance evaluation of IoT device lifetime typically considers only the battery state-of-charge and does not take into account the complex dynamics of the battery itself. Here, we present a IoT device lifetime model that more accurately reflects how batteries respond to IoT-typical loads.Our key insight is that an IoT device's power-saving sleep/wake cycle imposes intermittent pulse loads on the battery; the load duration and current depend on the operations(s) performed by the device during each awake interval. Our model therefore predicts the minimum output voltage that will be observed at the battery in response to a given pulse load. This value determines whether the battery is able to meet the input voltage requirements of the IoT device.We have developed an support vector regression (SVR)-based battery model using a dataset consisting of measurements of the voltage response of small lithium titanate (LTO) batteries to over 90K pulse loads. The model predicts the battery's minimum output voltage in response to a given pulse load with an RMAE (Root Mean Absolute Error) of less than 3% over the full operational range of the battery. The accuracy decreases at the more critical lower end of the voltage range, however, falling to ~ 8-10%RMAE.This pulse-response model allows us to create a "synthetic battery" and track its voltage response to a sequence of pulse loads, supporting a more realistic lifetime model where the IoT device fails as soon as the battery's predicted output voltage fails to meet its input voltage requirements.

Intrusion Detection Systems (IDS) are crucial for monitoring and managing critical infrastructure; however, their prominence makes them attractive targets for network attacks. Machine Learning (ML) techniques incorporated into IDS have shown promise in detecting and mitigating these threats. Unfortunately, the scarcity of attack samples presents challenges for model training and generalizability, and attackers can easily bypass detection systems by introducing temporal variations in their attacks. This paper develops strategies for detecting network attacks and specifically examines the impact of network configurations on the generalizability of detection models. As an illustrative example, we investigate the performance of Long Short-Term Memory (LSTM) models in capturing temporal changes in network behavior during attacks, and its resilience against distributional changes. Our study considers multiple factors in terms of attack types, variations, and network configurations, including different topologies and numbers of nodes. We provide insights into how these factors impact the generalizability of models trained using knowledge sharing. To support our research, we implemented Blackhole and DIS-flooding attack variations using the Cooja network simulator. Our objective was to generate a large dataset that enables a comprehensive analysis of attack variations across a diverse set of network configurations, focusing on the impact on LSTM-based IDS for IoT networks.

Radiometric fingerprinting is a promising passive security measure for low-power IoT devices, that exploits the hardware imperfections of their radio signals. In this paper, we focus on extracting radiometric fingerprints from Bluetooth Low Energy (BLE) devices. We depart from previous work in that we facilitate fingerprinting in embedded network scenarios by extracting features from I/Q samples collected using a widely used BLE System-on-Chip. We introduce a novel approach that leverages instantaneous frequency analysis of signal dynamics to reveal hardware imperfections during symbol transitions in the packet payload. Our objective is to identify the most significant features contributing to device identification. Our experimental evaluation demonstrates that augmenting traditional aggregated FFT-based features with our proposed transition-based features increases identification accuracy from 56% to 74%.

Innovative medical applications based on networked implants foster the development of in-body communication technologies. Among the in-body communication technologies that are being considered, fat intra-body communication (Fat-IBC) is a very recent approach. Its main advantage lies in its higher data rate compared to earlier approaches based on capacitive and galvanic coupling. However, Fat-IBC faces privacy-, security-, as well as safety-related attacks. In this paper, we discuss security and privacy concerns about Fat-IBC, as well as corresponding countermeasures. Furthermore, we present our secure protocol stack for Fat-IBC and suggest directions for future research.

Microwave communication through the fat tissue in the human body enables a new channel for wearable devices to communicate with each other. The wearable devices can communicate to the external world through a powerful device in their network called central control unit (CU); for example, a smartphone. Some wearable devices may be out of the range of the CU temporarily due to body movements or permanently due to low signal strength, in a fat channel communication network. Such devices can connect to the CU with the help of their neighbor device in the same network. In this paper, we propose a protocol to ensure secure indirect authentication and key establishment between the out-of-range device and the CU in a fat channel communication network, via an untrusted intermediate device in the network. The proposed protocol is lightweight and resistant to denial-of-sleep attacks on the intermediate device. We analyze the security and the computation overhead of the proposed protocol.

Password security relies heavily on the choice of password by the user but also on the one-way hash functions used to protect stored passwords. To compensate for the increased computing power of attackers, modern password hash functions like Argon2, have been made more complex in terms of computational power and memory requirements. Nowadays, the computation of such hash functions is performed usually by the server (or authenticator) instead of the client. Therefore, constrained Internet of Things devices cannot use such functions when authenticating users. Additionally, the load of computing such functions may expose servers to denial of service attacks. In this work, we discuss client-side hashing as an alternative. We propose Clipaha, a client-side hashing scheme that allows using high-security password hashing even on highly constrained server devices. Clipaha is robust to a broader range of attacks compared to previous work and covers important and complex usage scenarios. Our e valuation discusses critical aspects involved in client-side hashing. We also provide an implementation of Clipaha in the form of a web library 1 and benchmark the library on different systems to understand its mixed JavaScript and WebAssembly approach’s limitations. Benchmarks show that our library is 50% faster than similar libraries and can run on some devices where previous work fails.

Obtaining hands-on experience with wireless communication can be challenging due to the limited configurability of most commercial off-the-shelf transceivers. We present a low-cost and open-source educational platform designed to help students experiment and investigate wireless concepts through real-world testing. Our platform provides students with the ability to control the physical-layer configuration, design and implement their backscatter system, and conduct link quality experiments to investigate the resulting system performance. This platform offers students a unique opportunity to gain practical experience and deepen their understanding of wireless communication, while also providing a valuable tool for researchers and educators in the field.

Intrusion detection systems (IDS) are crucial components in a defense strategy for IoT networks, as such networks have applicability in safety-critical environments such as healthcare, manufacturing, and smart cities, to name but a few. A promising approach is to train IDS models using machine learning (ML) with data from previous attacks. Unfortunately, the models are only as good as the data provided in the training, and often access to realistic attack data is limited. In this paper we focus on Blackhole attacks in low-power and lossy IoT networks. Specifically, we study the impact of Blackhole attack variations on a ML-based IDS. We implemented the variation strategies in the Cooja network simulator, with the objective to create new data sets and to quantity the impact of an attack variation on the network and IDS model performance. Our initial results show that variations of the Blackhole attack have a negative impact on the performance of the IDS, and thus, paves the way forward for further research on how attack variations and corresponding data set complexity can improve the performance of an IDS.

Radiometric fingerprint schemes have been shown effective in identifying wireless devices based on imperfections in their hardware electronics. The robustness of fingerprint systems under complex channel conditions, however, is a critical challenge that makes their application in real-world scenarios difficult. We systematically evaluate the wireless channel's impact on radiometric fingerprints and find that the channel impacts fingerprint features in a very particular way that depends on the channel's properties. Based on the insights, we present RRF, a system that provides a robust identification/authentication service even under complex channel fading disturbance. Our design deploys a hybrid architecture that combines wireless channel simulation, signal processing and machine learning. In this pipeline, RRF first utilizes a series of structured channel simulations to strategically improve system tolerance towards multipath channel interference. On top of that, in the identification phase, RRF relies on noise compensation and a feature denoising filter to augment the system's stability in noisy conditions with weak signals. Our experimental results show that RRF achieves an average accuracy consistently above 99% in empirical scenarios with complex channels, where the baseline approach from previous work rarely exceeds 50%.

Network-based Intrusion Detection Systems (NIDSs) are an important mechanism to identify malicious behaviour or policy violations within a network. Such detection systems typically face several challenges, among which are the base-rate fallacy and the resilience against adaptive adversaries. These challenges are often countered in modern NIDSs by combining multiple detection systems to diversify the used feature levels or utilize the advantages of multiple detection methods. However, currently there exists no suitable framework for a detailed analysis of such composed systems. Therefore, the contribution of this work is an evaluation framework for composed systems, which builds on previous information-theoretic approaches and highlights the utility of information-theoretic redundancies for robustness evaluations. This framework enables an attribution of the overall system performance to its individual components, to fine-tune parameters and to study the dynamics between classifiers. The versatility of the framework is demonstrated by designing and evaluating a composed NIDS example based on systems described in the literature and using an open data set. Studying the impact of an evasion attempt with adversarial examples on this system highlighted the importance of robustness against false-alarms as well as detection evasion. Moreover, the framework enables general insights on how to improve the design of composed NIDSs: based on the dynamics between classifiers, it can be shown that optimizing the operation point of each component individually does not necessarily maximize the overall system performance from an information-theoretic perspective. Additionally, it can be shown that existing classification redundancies might not be fully utilized during an attack on the NIDS components, due to a static system design.

Edge computing aims to enable applications with stringent latency requirements, e.g., augmented reality, and tame the overwhelming data streams generated by IoT devices. A core principle of this paradigm is to bring the computation from a distant cloud closer to service consumers and data producers. Consequentially, the issue of edge computing facilities' placement arises. We present a comprehensive analysis suggesting where to place general-purpose edge computing resources on an Internet-wide scale. We base our conclusions on extensive real-world network measurements. We perform extensive traceroute measurements from RIPE Atlas to datacenters in the US, resulting in a graph of 11K routers. We identify the affiliations of the routers to determine the network providers that can act as edge providers. We devise several edge placement strategies and show that they can improve cloud access latency by up to 30%.

New battery-free sensor tags that interoperate with unmodified standard IoT devices and protocols can extend a sensor network's capabilities in a scalable and cost-effective manner. The tags achieve battery-free operation through backscatterrelated techniques, while the standard IoT devices avoid additional dedicated infrastructure by providing the unmodulated carrier that tags need to communicate. However, this approach requires coordination between devices transmitting, receiving and generating carrier, adds extra latency and energy consumption to already constrained devices, and increases interference and contention in the shared spectrum. We present a scheduling mechanism that optimizes the use of carrier generators, minimizing any disruptions to the regular nodes. We employ time slots to coordinate the unmodulated carrier while minimizing latency, energy consumption and overhead radio emissions. We propose an efficient scheduling algorithm that parallelizes communications with battery-free tags when possible and shares carriers among multiple tags concurrently. In our evaluation we demonstrate the feasibility and reliability of our approach in testbed experiments. We find that we can significantly reduce the excess latency and energy consumption caused by the addition of sensor tags when compared to sequential interrogation. We show that the gains tend to improve with the network size and that our solution is close to optimal on average.

New battery-free sensor tags that interoperate with unmodified standard IoT devices can extend a sensor network’s capabilities in a scalable and cost-effective manner. The tags achieve battery-free operation through backscatter-related techniques, while the standard IoT devices can provide the necessary unmodulated carrier, avoiding additional dedicated infrastructure. However, this approach presents multiple challenges: It requires coordination between nodes transmitting, receiving and generating carrier, adds extra latency and energy consumption to already constrained devices, and increases interference and contention in shared spectrum. We present TagAlong, a medium access mechanism for interoperable sensor tags that, besides coordinating, optimizes the use of carrier generators, minimizing the disruption caused to the operation of the regular nodes. We accomplish this by parallelizing communications with battery-free tags when possible, sharing carriers for multiple tags concurrently and synchronizing communications with tags that share carrier generators. We demonstrate the feasibility of TagAlong in a testbed deployment. In our evaluation we find that it can reduce the duration of the tags’ schedule by 60% while improving the energy and spectrum usage by 30% when compared to sequential interrogation with no difference in reliability.

In the near future more and more people will have multiple implants to handle their diseases. The implants benefit from being connected using in-body sensor networks. We have previously shown that RF communication through human adipose (fat) tissue is feasible. In this poster, we argue why we believe that backscatter communication within this fat channel is possible. As security is of utmost importance for in-body communication, we also discuss how backscatter-based in-body networks can be secured.

Radiometric signatures have been shown effective in identifying wireless devices, also known as fingerprinting, which refers to imperfections in their electronics. Previous work mainly considered static channel conditions. In this work, we systematically and experimentally study the impact of dynamic and complex channel conditions on the radiometric signatures. The results show a threat to identification accuracy for modulation error-based fingerprinting that was considered channel-resilient.

We consider large scale Internet of Things applications requesting data from physical devices. We study the problem of timely dissemination of sensor data towards applications with freshness requirements by means of a cache. We aim to minimize direct access to the possibly battery powered physical devices, yet improving Age of Information as a data freshness metric. We propose an Age of Information-aware scheduling policy for the physical device to push sensor updates to the caches located in cloud data centers. Such policy groups application requests based on freshness thresholds, thereby reduces the number of requests and threshold misses, and accounts for delay variation. The policy is incrementally introduced as we study its behavior over ideal and more realistic communication links with delay variation. We numerically evaluate the proposed policy against a simple yet widely used periodic schedule. We show that our informed schedule outperforms the periodic schedule even under high delay variations.

Wearable devices, such as implantable medical devices and smart wearables, are becoming increasingly popular with applications that vary from casual activity monitoring to critical medical uses. Unsurprisingly, numerous security vulnerabilities have been found in this class of devices. Yet, research on physical measurement-based authentication and key distribution assumes that body-worn devices are benign and uncompromised. Tiek is a novel authentication and key distribution protocol which addresses this issue. We utilize two sources of randomness to perform device authentication and key distribution simultaneously but through separate means. This creates a two-tier authorization scheme that enables devices to join the network while protecting them from each other. We describe Tiek and analyze its security.

Network operators are accustomed to use IP-layer active measurements for assessing end-to-end network performance and expect that new technology, such as IoT, provides similar means. This paper investigates the potential and also over-head associated with active measurements in IoT environments, with emphasis on the wireless part. An experimental approach provides insights into RTT accuracy for IoT devices, overhead in terms of energy consumption and network impact, and visualization of network-wide results for bottleneck localization.

Radio Tomographic Imaging (RTI) enables novel radio frequency (RF) sensing applications such as intrusion detection systems by observing variations in radio links caused by human actions. RTI applications are, however, severely limited by the requirement to retrofit existing infrastructure with energy-expensive sensors. In this demonstration, we present our ongoing efforts to develop the first battery-free RTI system that operates on minuscule amounts of energy harvested from the ambient environment. Our system eliminates the energy-expensive components employed on state-of-the-art RTI systems achieving two orders of magnitude lower power consumption. Battery-free operation enables a sustainable deployment, as RTI sensors could be deployed for long periods of time with little maintenance effort. Our demonstration showcases an intrusion detection scenario enabled by our system.

We describe a testbed for studying battery discharge behavior and the lifetime of wireless devices under controlled temperature conditions and present preliminary measurement results.

There is the long-standing assumption that radio communication in the range of hundreds of meters needs to consume mWs of power at the transmitting device. In this paper, we demonstrate that this is not necessarily the case for some devices equipped with backscatter radios. We present LOREA an architecture consisting of a tag, a reader and multiple carrier generators that overcomes the power, cost and range limitations of existing systems such as Computational Radio Frequency Identification (CRFID). LOREA achieves this by: First, generating narrow-band backscatter transmissions that improve receiver sensitivity. Second, mitigating self-interference without the complex designs employed on RFID readers by keeping carrier signal and backscattered signal apart in frequency. Finally, decoupling carrier generation from the reader and using devices such as WiFi routers and sensor nodes as a source of the carrier signal. An off-the-shelf implementation of LOREA costs 70 USD, a drastic reduction in price considering commercial RFID readers cost 2000 USD. LOREA's range scales with the carrier strength, and proximity to the carrier source and achieves a maximum range of 3.4 km when the tag is located at 1 m distance from a 28 dBm carrier source while consuming 70 μW at the tag. When the tag is equidistant from the carrier source and the receiver, we can communicate upto 75 m, a significant improvement over existing RFID readers.

We present LOREA an architecture consisting of a backscatter tag, a reader and multiple carrier generators that overcomes the power, cost and range limitations of existing backscatter systems such as Computational Radio Frequency Identification (CRFID). LOREA achieves this by: First, generating narrow-band backscatter transmissions. Second, by mitigating self-interference without the complex designs employed on RFID readers by keeping carrier signal and backscattered signal apart in frequency. Finally, by decoupling carrier generation from the reader and using devices such as WiFi routers and sensor nodes as a source of the carrier signal. LOREA's communication range scales with the carrier strength, and proximity to the carrier source and achieves a maximum range of 3.4km when the tag is located 1 m from the carrier source while consuming 70 μWs at the backscatter tag. We present various ultra-low power and long-range features of the LoRea architecture.

Visible Light Communication (VLC) is an emerging communication channel serving as a complement to traditional wireless communication. Visible light has many advantages over other communication channels like its inability to penetrate opaque objects. Securely sharing secret keys is a known problem in computer security. Sharing security keys using Radio Frequency (RF) communication is prone to sniffing attacks. In this paper, we introduce a system called BouKey which uses visible light to share keys. This makes BouKey immune to through the wall sniffing attacks. BouKey divides the key and shares it using multiple transmission bulbs allowing the key to be received in a specific area only. Our initial results show that the characteristics and height of the receiver, as well as the transmission angle play a key role in determining the shape and size of this area.

Visual codes are used to embed digital data in physical objects, or they are shown in video sequences to transfer data over screen/camera links. Existing codes either carry limited data to make them robust against a range of channel conditions (e.g., low camera quality or long distances), or they support a high data capacity but only work over a narrow range of channel conditions. We present FOCUS, a new code design that does not require this explicit trade-off between code capacity and the reader's channel quality. Instead, FOCUS builds on concepts from OFDM to encode data at different levels of spatial detail. This enables each reader to decode as much data from a code as its channel quality allows. We build a prototype of FOCUS devices and evaluate it experimentally. Our results show that FOCUS gracefully adapts to the reader's channel, and that it provides a significant performance improvement over recently proposed designs, including Strata and PixNet.

Outdoor wireless sensor networks are all exposed to a constantly changing environment that influences the performance of the network. In this paper, we study how variations in meteorological conditions influence IEEE 802.15.4 links. We show that the performance varies over both long and short periods of time, and correlate these variations to changes in meteorological conditions. The case study is based on six months of data from a sensor network deployed next to a meteorological research station running a continuous experiment, collecting both high-quality link and meteorological measurements. We present observations from the deployment, highlighting variations in packet reception ratio and signal strength. Furthermore, we show how the variations correlate with four selected meteorological factors, temperature, absolute humidity, precipitation and sunlight. Our results show that packet reception ratio and signal strength correlate the most with temperature and the correlation with other factors are less pronounced. We also identify a diurnal cycle as well as a seasonal variation in the packet reception ratio aggregated over all links. We discuss the implication of the findings and how they can be used when designing wireless sensor networks.

The paper proposes an innovative method for the performance analysis of opportunistic forwarding protocols over files logging mobile node encounters (contact traces). The method is modular and evolves in three steps. It first carries out contact filtering to isolate contacts that constitute message forwarding opportunities for givenmessage coordinates and forwarding rules. It then draws on graph expansion techniques to capture these forwarding contacts into sparse space-time graph constructs. Finally, it runs standard shortest path algorithms over these constructs and derives typical performance metrics such as message delivery delay and path hopcount. The method is flexible in that it can easily assess the protocol operation under various expressions of imperfect node cooperation. We describe it in detail, analyze its complexity, and evaluate it against discrete event simulations for three representative randomized forwarding schemes. The match with the simulation results is excellent and obtained with run times up to three orders of size smaller than the duration of the simulations, thus rendering our method a valuable tool for the performance analysis of opportunistic forwarding schemes.

In the emerging Delay Tolerant Networks (DTNs) content dissemination platform, mobile nodes opportunistically exchange content as they meet, with the intent of sharing content among nodes with common interests. During a meeting, nodes can exchange both content of direct interest to themselves as well as content that is of interest to a larger set of nodes that may be encountered in the future. The utility of a DTN is governed by the content exchange opportunity (the amount of content that can be exchanged during a meeting) as well as the selection of content to be exchanged in order to maximise the interest nodes will have in information they are exposed to. Considering that there is a cost associated with the content exchange (e.g. battery usage, buffer occupancy or consumed transmission opportunity) the aim for nodes participating in content dissemination should be to maximise their payoff. In this paper, we contribute a generic framework for describing the characteristics of content exchange among participating nodes in a network. We incorporate a distributed information popularity measurement and the pairwise interaction of nodes modelled as a bargaining problem. The outcome of this process is the fair split up of transmission opportunity as a network resource and the selection of content objects to exchange in order to maximise the nodes' payoff. The framework is generally intended as a capstone for investigation of content dissemination properties and various content exchange strategies in a DTN. The paper further presents experiments conducted to validate the function and correctness of the proposed framework.

The rising popularity of web services and their applications to sensor networks enables real-time data collection and queries by users. Unlike traditional periodic data collection, the traffic patterns generated from real-time data collection may expose the interests of users or the locations of unusual events to the attackers. To provide privacy in data collection, we propose a novel probabilistic sampling mechanism that can hide user queries and unusual events in the network, while supporting both routine and on-demand data reporting. Our goal is to prevent attackers from locating the unusual events and identifying interests of users by eavesdropping and analyzing the network traffic. In our probabilistic sampling scheme, the data are carefully reported at random times in order to mask the unusual events and user queries. In the meantime, our scheme can provide users with high data accuracy at minimized communication overheads. Extensive simulations have been conducted to evaluate the security strength, data accuracy and communication overheads of the proposed scheme.

We propose two strategies to reduce energy consumption for secure data aggregation in wireless sensor networks. While the purpose of data aggregation is to reduce energy consumption, secure data aggregation introduces an overhead making the total energy consumption comparable to naive secure non-aggregation. Another aspect is that the secure data aggregation has higher node congestion than non-aggregation schemes for networks of sizes up to about 100 nodes. Considering a secure data aggregation protocol, our strategies are to reduce the height of the aggregation tree and limit its node degree. The former results in fewer verification messages and a lower total communication load, and the latter reduces node congestion. The most heavily burdened node sends a third of the messages required in the original protocol, which for small networks is lower than with non-aggregation, and the total communication load is lowered compared to non-aggregation.

We demonstrate search-based data dissemination among mobile phones where content spreads according to how well it ranks against a user's declared interests. The dissemination is facilitated by Haggle -- a data-centric architecture for opportunistic networking environments. On top of Haggle we run an example application, called PhotoShare, which allows users to take photographs with the phone's camera that they can then share with other phones directly, over either WiFi or Bluetooth. In order to illustrate how Haggle disseminates the photos, we visualize each phone's internal state on an external screen. Through the visualization it is possible to see how search primitives, such as relative matching and ranking of content against keywords, are used in Haggle as means to determine the dissemination and forwarding of data.

In the LEAP framework for wireless sensor networks a set of keys is used to secure communication. LEAP distinguishes between unicast (pairwise) communication, group (cluster) communication and global (broadcast) communication. The keys used in pairwise communication are derived from an initial key KIthat nodes are equipped with prior to deployment and that is deleted after link setup. Further keys are distributed encrypted with these pairwise keys. It is a weakness that, if the initial key is ever disclosed, the whole network is compromised. To lower the threat of KIdisclosure, we present a KI-less scheme for key predistribution. Our scheme is based on random key predistribution, and proves to perform better in medium sized networks than previous proposals. It is resilient against node capture attacks and allows node to node authentication. Attacks against overlying protocols in the network are more difficult with this scheme. We have conducted computations to show the feasibility of our scheme for networks up to a size of 1000 nodes. By introducing a key reuse system we are able to increase the probability of a successful link setup. We have included a security analysis that discusses our scheme's resistance against commonly known attacks.

We present a protocol for query authentication in a sensor network where there is multi-hop communication and the queries are broadcasted by the base station into the network. Authenticating the queries is important so attackers cannot modify existing queries because this would lead to wrong readings; or insert new ones into the network because this would lead to waste of energy. We propose a layered query authentication protocol that ensures that, in the presence of less than n captured nodes, unauthorized queries are stopped after a small number of hops. When more than n nodes are captured, the unauthorized queries will only spread in one direction with a limited angle. Message authentication codes (MACs) are used to protect the authenticity and integrity of the query, n MACs are attached to the query message at the base station and the nodes replace MACs from this message in an interleaved manner.

By definition, the operation of an asynchronous power save protocol permits an arbitrary distribution of nodes' wakeup schedules. This wakeup schedule distribution creates an uncoordinated pattern of times at which nodes attempt to transmit. Intuitively, we would expect that some patterns are more (or less) favorable than others for a given traffic pattern. We investigate the impact of this wakeup pattern on network capacity and present simulation data showing that the capacity associated with the best wakeup patterns is significantly larger than that of the worst. This result not only gives insight to the behavior of such protocols, but also acts as a feasibility study showing the potential benefit of mechanisms by which nodes adapt their wakeup schedules to obtain improved performance.

We investigate application of agreement protocols tolerating malicious failures in sensor networks. We identify several scenarios where agreement can be used, and report on our experience with implementing an agreement protocol. The ultimate goal is to implement a secure and energy-efficient agreement protocol for sensor networks

The DEAPspace project is building an interaction framework for connecting pervasive devices over the wireless medium, supporting the development of new proximity-based collective distributed applications. The main components of this framework are the discovery algorithm and the service description model. DEAPspace provides devices with useful information about the other devices in their surroundings. This information can be queried in a consistent way that will tolerate evolutions, and allow legacy devices to continue to function in the fast-developing world of pervasive gadgets. The discovery is done in a power-efficient, and network-friendly way, and will adapt to a wide range of error conditions. This framework has been implemented, and allows the development of distributed applications that use ad-hoc transient networking as part of their function. The primary implementation was in Java. A subset of the code was also written in C, to allow the use of machines which do not have a JVM. In addition to simulation, the code has been tested over TCP/IP and the Ethernet interface of an 802.11 link.