Internet of Intelligent Reflecting Surfaces

• IoIRS is a networked architecture that coordinates programmable metasurfaces to control electromagnetic wave propagation for communication and sensing.
• It leverages protocolized control and multi-layer orchestration to optimize performance, enable secure sensing, and support covert communications in diverse environments.
• IoIRS faces practical challenges such as channel estimation overhead, distributed control complexity, and scalable optimization for multi-user, multi-band deployments.

The Internet of Intelligent Reflecting Surfaces (IoIRS) denotes a networked architecture in which many intelligent reflecting surfaces are no longer treated as isolated physical-layer accessories, but as coordinated, addressable, and programmable infrastructure for shaping electromagnetic propagation across communication and sensing systems. In this view, an IRS remains a reconfigurable metasurface composed of many nearly passive elements with controllable reflection coefficients, but IoIRS elevates such surfaces into a distributed system that spans the physical layer and upper network layers, supports multi-surface orchestration, and exposes capabilities such as frequency support, geometry, mobility, and power state to network control entities (Bilgen et al., 30 Nov 2025, Gong et al., 2019). Earlier IRS literature often studied single-IRS or few-IRS deployments without using the term “IoIRS,” yet multi-IRS communication, sensing, covert communication, and deployment studies already provide its technical substrate by treating surfaces as a programmable radio environment embedded in buildings, vehicles, UAVs, and indoor or urban infrastructure (Zheng et al., 2024, Mei et al., 2021).

1. Conceptual basis and historical emergence

IoIRS extends the “smart radio environment” paradigm in which wireless propagation is no longer modeled as an uncontrollable fading medium, but as an engineered substrate whose reflection properties are set by software-controlled metasurfaces. In the IRS literature, each surface is typically represented as a two-dimensional reconfigurable array of nearly passive scattering elements, each element imposing a complex coefficient $r_n=\rho_n e^{j\phi_n}$, collected into a diagonal matrix $$\Theta=\operatorname{diag}(\rho_1 e^{j\phi_1},\dots,\rho_N e^{j\phi_N})$$; under ideal constant-modulus assumptions, this reduces to $$\Theta=\operatorname{diag}(e^{j\theta_1},\dots,e^{j\theta_N})$$ (Gong et al., 2019). What changes in IoIRS is not this element-level model but the architectural role of the surface: instead of being configured ad hoc by a single transmitter, IRSs become a distributed infrastructure with network identity, shared control, and multi-service responsibilities (Bilgen et al., 30 Nov 2025).

The main motivation for this shift is the breakdown of transmitter-centric control as the number of IRSs and the number of transmitters grow. The explicit IoIRS architecture argues that a purely transmitter-centric design faces scalability and computational burden, control and signaling overhead, uncoordinated resource contention when many transmitters want the same IRS, limited global optimization, and severe mobility-induced dynamics when users or IRSs are mobile (Bilgen et al., 30 Nov 2025). This suggests that IoIRS is not merely a densification of IRS deployment; it is a control-plane redesign.

Several strands of pre-IoIRS work already map naturally into this perspective. Studies on secure sensing and covert communications treat multiple distributed IRSs as a coordinated “stealth cloud” or as a distributed fabric creating covert corridors (Zheng et al., 2024). Multi-reflection work treats IRSs as nodes in a reflection graph with explicit path selection and routing (Mei et al., 2021). Deployment studies for 6G IoT consider one IRS per micro-cell as the local design unit, implying a future network made of many such units (Mahbub et al., 2023, Mahbub et al., 2022). Optical wireless work similarly places a centralized controller above an AP–IRS–user system, which is readily generalized to a network orchestrator for multiple optical IRS nodes (Hamad et al., 2024).

2. Electromagnetic abstraction and channel models

At the physical level, IoIRS inherits standard IRS channel abstractions. For a single IRS assisting a link from Alice to a user $u$, the effective channel is written as

$$h_u^{\rm eff}=h_{A,u}+\mathbf{g}_u^T\boldsymbol{\Phi}\mathbf{f}_A,$$

where $h_{A,u}$ is the direct channel, $\mathbf{f}_A$ is the Alice–IRS channel, $\mathbf{g}_u$ is the IRS–user channel, and $$\boldsymbol{\Phi}=\operatorname{diag}(e^{j\theta_1},\dots,e^{j\theta_N})$$ is the IRS phase-shift matrix. For multiple distributed IRSs $\{\mathcal{I}_k\}_{k=1}^K$, the aggregate channel becomes

$$\Theta=\operatorname{diag}(\rho_1 e^{j\phi_1},\dots,\rho_N e^{j\phi_N})$$0

so each $$\Theta=\operatorname{diag}(\rho_1 e^{j\phi_1},\dots,\rho_N e^{j\phi_N})$$1 is a local control variable in a network-wide configuration vector (Zheng et al., 2024). Survey formulations use the same structure in MISO and MIMO form, e.g.

$$\Theta=\operatorname{diag}(\rho_1 e^{j\phi_1},\dots,\rho_N e^{j\phi_N})$$2

which is the natural matrix-level model for multi-IRS IoIRS systems (Gong et al., 2019).

The path-loss structure is central. Single-IRS models often exhibit the familiar $$\Theta=\operatorname{diag}(\rho_1 e^{j\phi_1},\dots,\rho_N e^{j\phi_N})$$3 gain under coherent combining, but with distance dependence determined either by a sum-distance or product-distance regime depending on the propagation model (Gong et al., 2019). In large-scale IoIRS design, product-distance effects dominate many cascaded links. For example, in a micro-cell positioning study, IRS-assisted received power scales as

$$\Theta=\operatorname{diag}(\rho_1 e^{j\phi_1},\dots,\rho_N e^{j\phi_N})$$4

where $$\Theta=\operatorname{diag}(\rho_1 e^{j\phi_1},\dots,\rho_N e^{j\phi_N})$$5 and $$\Theta=\operatorname{diag}(\rho_1 e^{j\phi_1},\dots,\rho_N e^{j\phi_N})$$6 are BS–IRS and IRS–user distances, respectively, and $$\Theta=\operatorname{diag}(\rho_1 e^{j\phi_1},\dots,\rho_N e^{j\phi_N})$$7 is the total number of elements (Mahbub et al., 2023). This directly explains why placement and routing are inseparable in IoIRS.

Multi-reflection generalizes the model from channel superposition to cascaded beam routing. For a reflection path $$\Theta=\operatorname{diag}(\rho_1 e^{j\phi_1},\dots,\rho_N e^{j\phi_N})$$8 of length $$\Theta=\operatorname{diag}(\rho_1 e^{j\phi_1},\dots,\rho_N e^{j\phi_N})$$9, the effective channel contains a product of inter-IRS channels and reflection matrices,

$$\Theta=\operatorname{diag}(e^{j\theta_1},\dots,e^{j\theta_N})$$0

and the full channel is the sum of direct, single-reflection, and multi-reflection contributions (Mei et al., 2021). Under LoS-dominant conditions, this leads to cooperative passive beamforming gains that scale rapidly with the number of reflections, but also to compounded path loss.

IoIRS is not restricted to RF phase-shift metasurfaces. In laser-based optical wireless communications, the IRS is modeled as a wall-mounted $$\Theta=\operatorname{diag}(e^{j\theta_1},\dots,e^{j\theta_N})$$1 mirror array of rotational mirrors with reflectivity $$\Theta=\operatorname{diag}(e^{j\theta_1},\dots,e^{j\theta_N})$$2, and the total user channel gain is

$$\Theta=\operatorname{diag}(e^{j\theta_1},\dots,e^{j\theta_N})$$3

where NLoS terms are created by AP $$\Theta=\operatorname{diag}(e^{j\theta_1},\dots,e^{j\theta_N})$$4 IRS mirror $$\Theta=\operatorname{diag}(e^{j\theta_1},\dots,e^{j\theta_N})$$5 user paths under specular reflection (Hamad et al., 2024). A plausible implication is that IoIRS should be understood as a modality-agnostic concept: RF metasurfaces, optical mirror arrays, and related programmable surfaces fit the same network abstraction as long as they expose controllable propagation states.

3. Architectures, control planes, and protocolization

Two architectural motifs recur throughout the literature. The first is the IRS as a local node with a controller that receives CSI or environmental information and programs the surface. The second is the network-level extension in which many such nodes are coordinated either centrally or in a distributed fashion. In secure sensing and covert communication work, the system is already described as a two-mode IRS platform: a sensing or reconnaissance mode estimates numbers and positions of radars or eavesdroppers and relevant path gains, followed by a reflection or anti-detection mode in which the controller updates $$\Theta=\operatorname{diag}(e^{j\theta_1},\dots,e^{j\theta_N})$$6 to realize stealth, spoofing, or covert communication (Zheng et al., 2024). This two-mode structure is a direct prototype for IoIRS operation.

The most explicit protocolized IoIRS architecture introduces five logical entities: transmitter, receiver, IRS Station (IRSS), IRS Node (IRSN), and IRS Server. Here IRSs are integrated into the network stack and addressed via IPv6; IRS Stations act as edge coordinators and decision engines; IRS Nodes are the physical metasurfaces with digital identities describing physical attributes, power profile, operative frequency index, reflective capabilities, mobility status, location, IP, and MAC; and the IRS Server acts as global administrator distributing protocol definitions and optimization-engine versions (Bilgen et al., 30 Nov 2025). Protocol A handles Tx–IRSS service requests, Protocol B supports IRSS–Rx localization and state acquisition, and Protocol C carries IRSS–IRSN actuation commands and IRSN status updates. This is the clearest formalization of IoIRS as a network-layer construct rather than a pure PHY abstraction.

The protocol view complements older hardware-control literature. Papers about IRS implementation describe each cell as connected to tunable chips controlled by local electronics and, at larger scales, by inter-cell wired or wireless control networks with NoC-like architectures, MAC protocols, and fault-tolerant routing (Gong et al., 2019). The gap between those hardware-level control fabrics and the IPv6-layer IRSS/IRSN architecture is organizational rather than conceptual: both assume that an IRS is an addressable, stateful node in a broader control system.

Centralized versus distributed orchestration remains a core design fork. Single-IRS studies usually assume BS-centric or local centralized control (Mahbub et al., 2023). Multi-IRS secure communication and deployment work explicitly generalize this to either a central controller or network orchestrator collecting multi-IRS CSI, or distributed controllers that coordinate by low-rate backhaul and approximate the global policy through consensus or distributed optimization (Zheng et al., 2024). This suggests that IoIRS control will likely be hierarchical: local controllers for fast adaptation and global orchestration for placement, policy, and long-timescale scheduling.

4. Optimization, placement, routing, and scaling laws

IoIRS design is dominated by coupled active–passive optimization. Across surveys and system papers, the canonical formulations include rate maximization,

$$\Theta=\operatorname{diag}(e^{j\theta_1},\dots,e^{j\theta_N})$$7

power minimization under QoS constraints,

$$\Theta=\operatorname{diag}(e^{j\theta_1},\dots,e^{j\theta_N})$$8

and weighted sum-rate, secrecy-rate, or max–min SINR objectives, all under unit-modulus or discrete-phase constraints on IRS coefficients (Gong et al., 2019). The algorithmic toolkit is correspondingly stable: alternating optimization or block coordinate descent, semidefinite relaxation, majorization–minimization, successive convex approximation, and manifold optimization recur throughout the literature (Gong et al., 2019, Zheng et al., 2024).

Large-scale IoIRS adds at least three additional optimization layers. First, practical hardware models become frequency dependent. In a multi-cell multi-band system, each IRS element is modeled by a parallel resonant circuit whose reflection coefficient

$$\Theta=\operatorname{diag}(e^{j\theta_1},\dots,e^{j\theta_N})$$9

depends on both capacitance $u$0 and frequency $u$1; this induces inter-frequency coupling and leads to an IRS indicator matrix $u$2 with $u$3, meaning each element can primarily serve at most one BS or band (Cai et al., 2021). Second, multi-IRS routing appears. In multi-reflection systems, the optimal reflection chain for a user can be cast as a shortest-path problem on a LoS graph, while the multi-user version with path-separation constraints becomes NP-complete and is approached via partial enumeration and beam-routing heuristics (Mei et al., 2021). Third, dynamic overhead becomes the bottleneck. A two-timescale design fixes IRS reflection patterns on a large timescale using statistical CSI and updates active beamformers and power allocation per slot using outdated instantaneous CSI, maximizing achievable average sum-rate while avoiding excessive cascaded-channel estimation; one implementation uses recursive sampling particle swarm optimization for the slow-timescale IRS pattern design (Cao et al., 2024).

Deployment results are unusually concrete. In a two-tier 6G-oriented network, a micro-cell IRS placed at $u$4 with the micro BS at $u$5 yields the best cell-edge SINR of $u$6, while reducing micro-BS transmit power from $u$7 to $u$8, i.e., a $u$9 reduction (Mahbub et al., 2023). For multi-reflection architectures, the trade-off is between compounded path loss and cooperative passive beamforming gain. Under LoS inter-IRS conditions, double-IRS gain can scale as $$h_u^{\rm eff}=h_{A,u}+\mathbf{g}_u^T\boldsymbol{\Phi}\mathbf{f}_A,$$0, while general $$h_u^{\rm eff}=h_{A,u}+\mathbf{g}_u^T\boldsymbol{\Phi}\mathbf{f}_A,$$1-reflection chains yield a gain term $$h_u^{\rm eff}=h_{A,u}+\mathbf{g}_u^T\boldsymbol{\Phi}\mathbf{f}_A,$$2; however, under NLoS inter-IRS conditions or small $$h_u^{\rm eff}=h_{A,u}+\mathbf{g}_u^T\boldsymbol{\Phi}\mathbf{f}_A,$$3, single reflections can outperform multi-reflection because the path-loss penalty dominates (Mei et al., 2021). A plausible implication is that IoIRS planning must optimize not only how many panels are deployed, but also whether they are used as parallel one-hop reflectors or as nodes in a deeper reflection route.

5. Services, application domains, and empirical evidence

IoIRS is best understood as a service fabric rather than a single use case. One major service family is secure sensing and covert communication. IRSs can cancel target echoes for electromagnetic stealth, shape angle-selective nulls toward multiple radars, redirect reflections to create decoys, and tune paths so that Bob sees constructive combining while Willie sees destructive combining; in the covert setting, randomized phase patterns also inject uncertainty into the warden’s observations (Zheng et al., 2024). Security-specific work further shows that perfect covertness is impossible in a single-antenna system without IRS but is achievable with IRS when the effective Alice–IRS–Willie link is at least as strong as the Alice–Willie channel, and that the common assumption $$h_u^{\rm eff}=h_{A,u}+\mathbf{g}_u^T\boldsymbol{\Phi}\mathbf{f}_A,$$4 is not always optimal because security and privacy objectives may require element-wise amplitude adaptation (Yan et al., 2021). This suggests that IoIRS may function as a network-wide physical-layer privacy infrastructure, not merely as a rate booster.

A second family is 6G IoT and massive access. IRS positioning for micro-cells explicitly targets cell-edge IoT devices and shows that one properly placed IRS can increase SINR while lowering BS power by $$h_u^{\rm eff}=h_{A,u}+\mathbf{g}_u^T\boldsymbol{\Phi}\mathbf{f}_A,$$5 (Mahbub et al., 2023). IRS-assisted IoT surveys connect such gains to mMTC, eMBB, and URLLC requirements and treat IRS-enhanced micro-cells as a natural precursor to an IoIRS fabric spanning macro and micro tiers (Mahbub et al., 2022). In massive device connectivity, IRS-assisted uplink random access leads to a joint activity detection and channel estimation problem over a cascaded BS–IRS–device channel, framed as sparse matrix factorization, matrix completion, and multiple measurement vectors, with a three-stage approximate-message-passing framework (Xia et al., 2019). The common thread is that IoIRS is naturally aligned with sparse, low-power, coverage-challenged access.

A third family is sensing and ISAC. IRS-aided sensing work distinguishes passive, semi-passive, and active sensing IRSs, with active sensing—where the IRS controller becomes a transmitter and sensors are placed on the surface—showing the highest received power and the lowest DoA estimation RMSE among the three architectures in the reported LoS scenario (Shao et al., 2023). IRS-assisted ISAC surveys describe common IRS, dedicated IRS, and RCC deployments, and report examples such as about $$h_u^{\rm eff}=h_{A,u}+\mathbf{g}_u^T\boldsymbol{\Phi}\mathbf{f}_A,$$6 SNR gain for IRS-aided MIMO radar, about $$h_u^{\rm eff}=h_{A,u}+\mathbf{g}_u^T\boldsymbol{\Phi}\mathbf{f}_A,$$7 sum-rate or spectral-efficiency improvements in some DFRC settings, and gains from double-IRS DFRC over single-IRS baselines (Elbir et al., 2022). In a broad IoIRS reading, these results mean that distributed surfaces can be scheduled as communication enhancers, radar apertures, localization landmarks, or mixed ISAC resources depending on context.

Optical and coded-network extensions broaden the scope. In indoor laser-based optical wireless communication, deploying a $$h_u^{\rm eff}=h_{A,u}+\mathbf{g}_u^T\boldsymbol{\Phi}\mathbf{f}_A,$$8 IRS mirror array yields $$h_u^{\rm eff}=h_{A,u}+\mathbf{g}_u^T\boldsymbol{\Phi}\mathbf{f}_A,$$9 higher sum rate than a $h_{A,u}$0 array and $h_{A,u}$1 higher sum rate than the system without IRS, showing that the IoIRS concept survives translation into specular optical environments (Hamad et al., 2024). In an IRS-aided butterfly network with physical-layer network coding, a 32-element IRS reduces relay BER by three orders of magnitude relative to the no-IRS case (Kafizov et al., 2021). Field trials add real-world support: at $h_{A,u}$2, a two-IRS deployment improved RSRP from $h_{A,u}$3 to $h_{A,u}$4 dB for one user and from $h_{A,u}$5 to $h_{A,u}$6 dB for another, while downlink throughput increased from $h_{A,u}$7 to $h_{A,u}$8 Mbps and from $h_{A,u}$9 to $\mathbf{f}_A$0 Mbps, respectively (Wu et al., 15 Jan 2025).

6. Implementation limits, misconceptions, and open research directions

Several recurrent simplifications in the literature become problematic at IoIRS scale. One is the assumption of ideal, frequency-flat, unit-amplitude reflection. Practical multi-band IRS models show strong frequency dependence, so an element configured for one band may present nearly fixed phases to other bands (Cai et al., 2021). Security-oriented work shows that amplitudes are not always optimally set to one (Yan et al., 2021). Another is the assumption that one surface suffices. Multi-IRS studies show that single-IRS designs can be rank-deficient or interference-limited where double- or multi-IRS configurations recover multiplexing capability and fairness (Mei et al., 2021, Cao et al., 2024). A third is the assumption that passive reflection is intrinsically one-sided. Related work on the intelligent omni-surface introduces a reflective–transmissive metasurface serving users on both sides, with a tunable reflection–transmission power ratio $\mathbf{f}_A$1, and thus extends the surface-network concept beyond the one-sided IRS model (Zhang et al., 2020).

Channel acquisition and control overhead remain the hardest engineering barrier. Single-IRS MMSE training protocols already require $\mathbf{f}_A$2 sub-phases to estimate direct and cascaded channels element by element, and the overhead grows linearly with the number of elements; naive extension to many IRSs is therefore untenable (Nadeem et al., 2019). Multi-IRS and ISAC systems further multiply the number of cascaded links, users, targets, and time-varying states (Elbir et al., 2022, Shao et al., 2023). This is why two-timescale optimization, group-based estimation, beam training, statistical-CSI designs, and passive CSI inference from sensing modalities such as images are treated as essential, not optional, directions (Cao et al., 2024, Yan et al., 2021).

Implementation realism also tempers deployment optimism. Field tests show that semi-static IRS configurations can underperform expectations when paired with fast-varying massive-MIMO precoding, because the BS adapts every few milliseconds while the IRS remains comparatively static (Wu et al., 15 Jan 2025). Security likewise cuts both ways: the same programmable surfaces that can create protected zones and covert corridors also introduce new attack surfaces if controllers or metasurface programming are compromised (Zheng et al., 2024, Bilgen et al., 30 Nov 2025). Near-field operation, hardware impairments, quantized phase shifts, environmental robustness, control-plane latency, and SWaP constraints for aerial or spaceborne IRSs are repeatedly identified as open issues (Bilgen et al., 30 Nov 2025, Wu et al., 15 Jan 2025).

Current research directions therefore converge on a relatively coherent IoIRS agenda: network-wide anti-detection frameworks, distributed control and multi-agent learning, geometry-assisted and learning-based CSI, hybrid passive–active surfaces, movable or rotatable IRSs, near-field modeling, graph-based multi-IRS routing, secure control planes, and standardization of IRS identity and control interfaces (Zheng et al., 2024, Bilgen et al., 30 Nov 2025, Wu et al., 15 Jan 2025). Taken together, these directions imply that IoIRS is evolving from a metaphor for “many IRSs” into a concrete networking problem: how to make programmable surfaces discoverable, shareable, scalable, and trustworthy across communication, sensing, and privacy services.